Utilize brain-wide gene expression databases to identify regions that contain molecular signatures of adult neurogenesis.
Using Table S1 (or some other curated list), search the ABA for “neurons in waiting.”
Extend the analysis to include human brain. I’m not sure if the gene list from figure 5 in the paper you attached could be used directly (since the samples we collected from human were whole dentate gyrus, if I am remembering correctly), but that would make a good starting point.
A similar option would be to do a deconvolution analysis on RNA-Seq data from the BrainSpan atlas using the overlapping mouse/NHP[/human?] neurogenesis list as marker genes for SGZ. In theory we could show the relative decrease in SGZ neurons over time and also show that it doesn’t reach 0 (as long as we get any reads from these genes in the oldest donors).
Use sc-RNA-seq gene list to find similar patterns of expression across any of the ABA datasets.
library(pander)
setwd("~/Desktop/Research/Signatures_of_Neurogensis/")
# Mouse SGZ & GCL: GSE39697
load("/Users/schilder/Desktop/Dissertation/Gene_Expression/Raw data/Microarray/Full_Human_Microarray/All_ABA_humans.mni.RData")
# Set up eset
sampleInfo.hum <-subset(sampleInfo.hum_all, select= -c(mni_x,mni_y,mni_z))
# Make all cols factors, & Reorder names alphabetically
sampleInfo.hum <- lapply(sampleInfo.hum, factor)
sampleInfo.hum <- sampleInfo.hum[order(names(sampleInfo.hum))]
library(annotate)
# Set up phenotype data
pdata <- AnnotatedDataFrame(data.frame(sampleInfo.hum))
sampleNames(pdata) <- sampleInfo.hum$unique_id
# Create ExpressionSet
ABA_hum <- ExpressionSet(datExpr.hum_all, phenoData = pdata) #Correct this annot
# Add treatment condition
ABA_hum$treatment <- "Human"
library(WGCNA)
#mac_expression <- read.csv("Full Macaque Microarray/expression_matrix.csv", header=FALSE, row.names=1)
#mac_sampleinfo <- read.csv("Full Macaque Microarray/columns_metadata.csv", header=TRUE)
#mac_probes <- read.csv("Full Macaque Microarray/rows_metadata.csv", header=TRUE)
#save(mac_expression, mac_sampleinfo, mac_probes, file="Macaque Data.RData")
# Saved as R objects to speed up importing data
load("~/Desktop/Dissertation/Gene_Expression/Raw data/Microarray/Macaque Data.RData")
datExpr <- mac_expression
probeInfo <- mac_probes
sampleInfo <- mac_sampleinfo
rm(mac_expression,mac_probes,mac_sampleinfo)
# Select only probes with associated entrez genes
rownames(datExpr) <- probeInfo$probe_name
validProbes = which(probeInfo$entrez_id>=0,arr.ind=TRUE)
datExpr <- datExpr[validProbes,]
probeInfo <- probeInfo[validProbes,]
# Some genes have more than one probe. The collapseRows function chooses a single probe to represent a gene.
probeInfo$gene_symbol <-gsub("[()]","",probeInfo$gene_symbol) # Gets rid of weird parentheses
probeNames = probeInfo[which(probeInfo$probe_name == rownames(datExpr)),"gene_symbol"]
probeIDs = rownames(datExpr)
datCollapsed = WGCNA::collapseRows(datExpr, probeNames, probeIDs) # Collapse
datExpr <- datCollapsed$datETcollapsed
probeInfo <- probeInfo[which(datCollapsed$selectedRow==T, arr.ind=T),]
save(datExpr, probeInfo, sampleInfo,file="ABA.macaque_preprocessed.v4.RData")
load("~/Desktop/Research/Signatures_of_Neurogensis/ABA.macaque_preprocessed.v4.RData")
datExpr.mac <-datExpr
probeInfo.mac <-probeInfo
sampleInfo.mac <-sampleInfo
rm(datExpr,probeInfo,sampleInfo)
library(annotate)
# Create eset
ex_mac <- ExpressionSet(datExpr.mac)
# Create macaque phenoData:
sampleInfo.mac$unique_id <- factor(paste(sampleInfo.mac$donor_name, sampleInfo.mac$structure_acronym, sampleInfo.mac$well_id, sep="_"))
p <- data.frame(sampleInfo.mac)
rownames(p) <- sampleInfo.mac$unique_id
pdata <- AnnotatedDataFrame(p)
# Replace phenoData in mac ExpressionSet
phenoData(ex_mac) <- pdata
# Insert condition
ex_mac$treatment <- "Macaque"
ex_mac <-ex_mac[,colSums(is.na(exprs(ex_mac))) == 0] # Remove cols with NA
rm(datExpr.mac,probeInfo.mac,sampleInfo.mac,p,pdata)
library(readxl)
# S1: Genes differentially expressed between mouse SGZ and GCL
S1 <- data.frame(read_excel("~/Desktop/Research/Signatures_of_Neurogensis/Miller_2013/DEV097212 TableS1.xlsx",na = "NA"))
# Get rid of stars
S1$MouseGene <-gsub("[*]","",S1$MouseGene)
unique(S1$likelyCellType)
## [1] "Immature Neuron" "Dividing Cell"
## [3] "Red Blood Cell" "Vascular Endothelial Cell"
## [5] "Oligodendrocyte" "Astrocyte"
## [7] "Hilar Interneuron" "GABA Neuron"
## [9] "(undetermined)"
## Get gene list
immature.neuron <- S1[S1$likelyCellType=="Immature Neuron",]$MouseGene
dividing.cell <- S1[S1$likelyCellType=="Dividing Cell",]$MouseGene
S1.genes <- S1$MouseGene
# Convert from mouse to human genes
library(EWCE)
data("mouse_to_human_homologs")
m2h = unique(mouse_to_human_homologs[,c("HGNC.symbol","MGI.symbol")])
s1.genes <- unique(m2h[m2h$MGI.symbol %in% S1.genes,"HGNC.symbol"])
paste(sum(s1.genes %in% row.names(exprs(ex_mac))),"/",length(S1.genes),
"'Mouse SGZ-enriched' genes found in ABA Macaque microarray dataset.")
## [1] "250 / 363 'Mouse SGZ-enriched' genes found in ABA Macaque microarray dataset."
# S3: Mouse network modules
mouse_modules <- data.frame(read_excel("~/Desktop/Research/Signatures_of_Neurogensis/Miller_2013/DEV097212 TableS3.xlsx",na = "NA"))
## Subset specific modules
### tan module was associated with "neurogenesis" in Miller et al. 2013.
Mouse_mod.tan <- subset(mouse_modules, Module=="tan")$HumanOrtholog
paste(sum(Mouse_mod.tan %in% row.names(exprs(ex_mac))),"/",length(Mouse_mod.tan),"Mouse Tan module genes found in ABA Macaque microarray dataset.")
## [1] "21 / 26 Mouse Tan module genes found in ABA Macaque microarray dataset."
# S4: Macaque network modules
macaque_modules <- data.frame(read_excel("~/Desktop/Research/Signatures_of_Neurogensis/Miller_2013/DEV097212 TableS4.xlsx", na="NA"))
## Subset specific modules
### tan module was associated with "neurogenesis" in Miller et al. 2013.
Mac_mod.tan <- subset(macaque_modules, Module=="tan")$Gene
paste(sum(Mac_mod.tan %in% row.names(exprs(ex_mac))),"/",length( Mac_mod.tan),"Macaque Tan module genes found in ABA Macaque microarray dataset.")
## [1] "159 / 226 Macaque Tan module genes found in ABA Macaque microarray dataset."
# Perform DGE on Macaque SGZ vs. GCL
SGZ.samples <-ex_mac[,pData(ex_mac)$structure_acronym==c("DGsg") ]
GCL.samples <-ex_mac[,pData(ex_mac)$structure_acronym==c("DGgr") ]
# Limma
library(limma); library(statmod)
limma.data <-cbind(exprs(SGZ.samples), exprs(GCL.samples))
fit <-lmFit(limma.data, design=
c(rep(1,ncol(exprs(SGZ.samples))), rep(-1,ncol(exprs(GCL.samples)))) )
fit <- eBayes(fit)
SGZ.vs.GCL.results <- topTable(fit, number=Inf)
# Pair-wise t-test
# t.test(exprs(SGZ.samples), exprs(GCL.samples), paired=T)
sig.DEGS <-row.names(subset(SGZ.vs.GCL.results, adj.P.Val<=0.05))
paste("There are",length(sig.DEGS),"significant DEGs between Macaque SGZ and GCL.")
## [1] "There are 0 significant DEGs between Macaque SGZ and GCL."
# Top logFC genes
topTable(fit, number=10, sort.by = "logFC")
## logFC AveExpr t P.Value adj.P.Val B
## PDE7B -0.9180228 8.713520 -0.7460964 0.4587068 0.9997796 -4.597508
## SLFN13 -0.9097828 9.888308 -0.6544719 0.5154671 0.9997796 -4.598198
## LOC696983 -0.8983702 9.857653 -0.6475556 0.5198988 0.9997796 -4.598247
## SV2B -0.8928369 8.605551 -0.7263575 0.4706225 0.9997796 -4.597664
## CD36 -0.8237889 6.117680 -0.9247264 0.3590467 0.9997796 -4.595916
## LOC712107 -0.7955258 9.041498 -0.6338976 0.5287094 0.9997796 -4.598341
## ACVR1C -0.7874374 7.286313 -0.7568770 0.4522729 0.9997796 -4.597422
## C2orf55 -0.7801166 10.330626 -0.5429380 0.5893111 0.9997796 -4.598920
## ANO3 -0.7715562 10.594549 -0.5300162 0.5981805 0.9997796 -4.598995
## PCSK2 -0.7449458 8.575641 -0.6173636 0.5394787 0.9997796 -4.598453
library(dplyr); library(ggplot2)
candidate.genes <- ex_mac[row.names(exprs(ex_mac)) %in% c("SOX11","SOX4","DCX","MKI67")][,pData(ex_mac)$age=="48 mo"]
copy.mac <- data.frame(t(data.frame(exprs(candidate.genes))))
#copy.mac$Region <-sub(".*_ *(.*?) *_.*", "\\1", colnames(exprs(candidate.genes)))
copy.mac$structure_name <-pData(candidate.genes)$structure_name
copy.mac$structure_acronym <-pData(candidate.genes)$structure_acronym
candidate.data <-copy.mac %>% group_by(structure_name,structure_acronym) %>% summarise(SOX11=mean(SOX11), DCX=mean(DCX), MKI67=mean(MKI67)) %>% data.frame()
# SOX11
data <-candidate.data[order(-candidate.data$SOX11),]
## All regions
ggplot(data) + geom_bar(aes(x=structure_name, y=SOX11, fill=SOX11),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))
# Top 10 expression
ggplot(rbind(subset(data,structure_acronym!="DGsg" & structure_acronym!="DGgr")[1:10,], subset(data,structure_acronym=="DGsg"|structure_acronym=="DGgr"))) + geom_bar(aes(x=structure_name, y=SOX11, fill=SOX11),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))+ labs(title="Regions with the top 10 expression:\nSOX11")
# DCX
data <-candidate.data[order(-candidate.data$DCX),]
## All regions
ggplot(data) + geom_bar(aes(x=structure_name, y=DCX, fill=DCX),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))
## Top 10 expression
ggplot(rbind(subset(data,structure_acronym!="DGsg" & structure_acronym!="DGgr")[1:10,], subset(data,structure_acronym=="DGsg"|structure_acronym=="DGgr"))) + geom_bar(aes(x=structure_name, y=DCX, fill=DCX),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))+ labs(title="Regions with the top 10 expression:\nDCX")
# MKI67
data <-candidate.data[order(-candidate.data$MKI67),]
## All regions
ggplot(data) + geom_bar(aes(x=structure_name, y=MKI67, fill=MKI67),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5))
## Top 10 expression
ggplot(rbind(subset(data, structure_acronym!="DGsg" & structure_acronym!="DGgr")[1:10,], subset(data,structure_acronym=="DGsg"|structure_acronym=="DGgr"))) + geom_bar(aes(x=structure_name, y=MKI67, fill=MKI67),stat='identity') + theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1),plot.title = element_text(hjust=0.5)) + labs(title="Regions with the top 10 expression:\nMIK67")
first.PC <-1; last.PC <-4
# Run PCA
# full_PCA <- prcomp(exprs(ex_mac), scale.=T, center=F)
# save(full_PCA, file="all.genes_PCA.rda")
load("all.genes_PCA.rda")
# Get proportion of variance
per_PC1 <- round(summary(full_PCA)$importance[2,1]*100,2)
per_PC2 <- round(summary(full_PCA)$importance[2,2]*100,2)
# Create data.frame
full_PCA.data <- data.frame(full_PCA$rotation[,first.PC:last.PC])
full_PCA.data$Region <- pData(ex_mac)$structure_acronym
full_PCA.data$Age <- factor(pData(ex_mac)$age, c("E40","E50","E70","E80","E90","E120","0 mo","3 mo","12 mo","48 mo"),ordered=T)
# plot PCA
dat <-full_PCA.data
DGsg <-subset(dat,Region=="DGsg")
# By Region
### Plot
library(ggplot2)
ggplot(data=dat) + geom_point(aes(x=PC1, y=PC2, fill=Region, color=Region), size=2) +
geom_point(data=DGsg, aes(x=PC1, y=PC2, colour=Region), size=4, shape=15) +
geom_point(data=DGsg, aes(x=PC1, y=PC2), colour="black", size=4, shape=0) + labs(title="PCA: Colored by Region \n[SGZ samples outlined]", x=paste("PC1 (",per_PC1,"%)"), y=paste("PC2 (",per_PC2,"%)")) + theme(plot.title = element_text(hjust=0.5), legend.position="None")
## By Age
ggplot(data=dat) + geom_point(aes(x=PC1, y=PC2, fill=Age, color=Age), size=2) +
geom_point(data=DGsg, aes(x=PC1, y=PC2,colour=Age), size=4, shape=15) +
geom_point(data=DGsg, aes(x=PC1, y=PC2),colour="black", size=4, shape=0) + labs(title="PCA: Colored by Age \n[SGZ samples outlined]", x=paste("PC1 (",per_PC1,"%)"), y=paste("PC2 (",per_PC2,"%)")) + theme(plot.title = element_text(hjust=0.5))
# Get top PC1 loadings
PC1.load <-full_PCA$x[order(-abs(full_PCA$x[,1])),][,"PC1"]
write.csv(PC1.load[1:100],"PC1.allgenes.csv")
pander(PC1.load[1:10] , style='simple',justify='left')
| LOC701831 | RPLP0 | LOC711964 | RPS16 | ACTG1 | LOC708858 | LOC710110 |
|---|---|---|---|---|---|---|
| -79.14 | -78.52 | -78.51 | -78.31 | -78.24 | -77.7 | -77.66 |
| RPS11 | LOC697734 | EEF1A1 |
|---|---|---|
| -77.64 | -77.61 | -77.59 |
library(cluster); library(factoextra); library(magrittr)
# [1: Distance Methods]
unsup.clust_distance <- function(data){
res.dist <- get_dist(data, stand=T, method="pearson")
library(d3heatmap) # use interactive heatmap instead
d3heatmap(res.dist, scale = "column", k_row=num_clusts, k_col=num_clusts)
#fviz_dist(res.dist, gradient=list(low="#00AFBB", mid="white", high="#FC4E07"))
}
# [2: Partitioning Methods]
# [[K-means clustering]]
unsup.clust_kmeans <-function(data.){
#library("NbClust")
#nb <- NbClust(data., distance="euclidean", min.nc=2, max.nc=10, method="kmeans")
library("factoextra")
# Conduct kmean using that number
set.seed(123)
km.res <- kmeans(data., num_clusts, nstart = 25) # Automatically extract # of clusters
## Visualize
fviz_cluster(km.res, data = data.,
ellipse.type = "convex",
palette = "jco",
ggtheme = theme_minimal(), repel=F)
}
# [[Partitioning Around Medoids (PAM) clustering]]
unsup.clust_PAM <-function(data.){
library("cluster")
pam.res <- pam(data., num_clusts)
## Visualize
fviz_cluster(pam.res, data = data.,
ellipse.type = "convex",
palette = "jco",
ggtheme = theme_minimal(), repel=T)
}
# [Hierarchical Methods]
# [[Hierarchical Clustering]]
unsup.clust_hierarchical <- function(data){
library("factoextra")
res.hc <- data %>%
scale() %>% # Scale the data
dist(method = "euclidean") %>% # Compute dissimilarity matrix
hclust(method = "ward.D2") # Compute hierachical clustering
# Visualize using factoextra
# Cut in 4 groups and color by groups
fviz_dend(res.hc, k = num_clusts, # Cut in (#) groups
cex = 0.5, repel=F, # label size
palette="aaas",
color_labels_by_k =T, # color labels by groups
rect=T, # Add rectangle around groups
type="circular", #"rectangle", "circular", "phylogenic".
phylo_layout="layout.auto",# layout.auto, layout_with_drl, layout_as_tree, layout.gem, layout.mds, layout_with_lgl
horiz=T, main="Unsupervised Hierarchical Clustering"
)
assign("hierarchical.output",res.hc)
}
# [[Hierarchical Clustering on Principal Components (HCPC)]]
unsup.clust_hcpc <- function(facto_data){
library(FactoMineR)
# Principal Component Analysis:
res.pca <- PCA(facto_data, graph=F)
plot(res.pca)
# Clustering, auto nb of clusters:
hc <- HCPC(res.pca, nb.clust= -1)
hc$call$t$nb.clust # Number of suggest clusters
# Construct a hierarchical tree from a partition (with 10 clusters)
# (useful when the number of individuals is very important)
# hc2 <- HCPC(iris[,1:4], kk=10, nb.clust=-1)
}
# Setup data
unsup.eset <-ex_mac[,pData(ex_mac)$age=="48 mo"]
my_data <- t(exprs(unsup.eset))
row.names(my_data) <-make.unique(as.character(pData(unsup.eset)$structure_name))
# my_data <-full_PCA.data[,first.PC:last.PC]
# row.names(my_data) <-make.unique(as.character(full_PCA.data$Region))
# Estimate number of clusters
# est.clusts <-fviz_nbclust(my_data, kmeans, method = "gap_stat") # [0]
# save(est.clusts, file="estimated.clusters_all.genes.rda")
load("estimated.clusters_all.genes.rda")
est.clusts
num_clusts=5 # Select number of clusters based on results
# Conduct unsupervised clustering
unsup.clust_distance(my_data) # [1]
# unsup.clust_kmeans(my_data) # [2]
# unsup.clust_PAM(my_data) #[3]
# unsup.clust_hierarchical(my_data) #[4]
# unsup.clust_hcpc(my_data) # [5]
# Setup data
unsup.eset <-ex_mac[row.names(exprs(ex_mac)) %in% s1.genes, pData(ex_mac)$age=="48 mo"]
my_data <- t(exprs(unsup.eset))
row.names(my_data) <-make.unique(as.character(pData(unsup.eset)$structure_name))
# my_data <-full_PCA.data[,first.PC:last.PC]
# row.names(my_data) <-make.unique(as.character(full_PCA.data$Region))
# Estimate number of clusters
est.clusts_S1.genes <-fviz_nbclust(my_data, kmeans, method = "gap_stat") # [0]
est.clusts_S1.genes
num_clusts=3 # Select number of clusters based on results
# Conduct unsupervised clustering
unsup.clust_distance(my_data) # [1]
# unsup.clust_kmeans(my_data) # [2]
# unsup.clust_PAM(my_data) #[3]
# unsup.clust_hierarchical(my_data) #[4]
# unsup.clust_hcpc(my_data) # [5]
# Setup data
unsup.eset <-ex_mac[row.names(exprs(ex_mac)) %in% Mac_mod.tan, pData(ex_mac)$age=="48 mo"]
my_data <- t(exprs(unsup.eset))
row.names(my_data) <-make.unique(as.character(pData(unsup.eset)$structure_name))
# my_data <-full_PCA.data[,first.PC:last.PC]
# row.names(my_data) <-make.unique(as.character(full_PCA.data$Region))
# Estimate number of clusters
est.clusts_S4.genes <-fviz_nbclust(my_data, kmeans, method = "gap_stat") # [0]
est.clusts_S4.genes
num_clusts=5 # Select number of clusters based on results
# Conduct unsupervised clustering
unsup.clust_distance(my_data) # [1]
# unsup.clust_kmeans(my_data) # [2]
# unsup.clust_PAM(my_data) #[3]
# unsup.clust_hierarchical(my_data) #[4]
# unsup.clust_hcpc(my_data) # [5]
library(limma); library(statmod)
SGZ.samples <-exprs(ex_mac[,pData(ex_mac)$structure_acronym==c("DGsg")& pData(ex_mac)$age=="48 mo"])
region_list <- as.character(unique(pData(ex_mac)$structure_name))
DGE_results <- NULL
DGE_summary <- data.frame()
for(region in region_list){
region.samples <-exprs( ex_mac[,pData(ex_mac)$structure_name==region & pData(ex_mac)$age=="48 mo"] )
limma.data <-cbind(SGZ.samples, region.samples)
fit <-lmFit(limma.data, design=
c(rep(1,ncol(SGZ.samples)), rep(-1,ncol(region.samples))) )
fit <- eBayes(fit)
tab <- topTable(fit, number=Inf)
DGE_results[[paste("SGZ vs.",region)]] <- list(tab)
# Create summary table
sig <-dim(subset(tab, adj.P.Val<=0.05))[1]
total <- dim(tab)[1]
percent_DEGs <-round(dim(subset(tab, adj.P.Val<=0.05))[1] /
dim(tab)[1] *100,3)
DGE_summary <- rbind(DGE_summary,
data.frame(Test=paste("SGZ vs.", region), Sig_DEGs=sig,
Total_genes=total, Percent_DEGs=round(sig/total*100,3),
Total_logFC=sum(abs(tab$logFC)),
Total_t.stat=sum(abs(tab$t)))
)
}
# Print written summary
no_DEGs <-length(subset(DGE_summary, Sig_DEGs==0)$Sig_DEGs)
total_regions <-length(DGE_summary$Test)
paste(no_DEGs,"/",total_regions,"adult brain regions contained no Differentially Expressed Genes compared to adult SGZ.")
## [1] "61 / 128 adult brain regions contained no Differentially Expressed Genes compared to adult SGZ."
# Results table
dge <-subset(DGE_summary,Test!="<NA>")
pander(dge[order(c(dge$percent_DEGs, dge$Total_logFC)),], style='simple', justify='left', split.table=Inf)
| Test | Sig_DEGs | Total_genes | Percent_DEGs | Total_logFC | Total_t.stat | |
|---|---|---|---|---|---|---|
| 69 | SGZ vs. subgranular zone of dentate gyrus (cortex) | 0 | 17555 | 0 | 8.799e-12 | 2.409e-12 |
| 73 | SGZ vs. granular layer of dentate gyrus (cortex) | 0 | 17555 | 0 | 1579 | 433.1 |
| 70 | SGZ vs. stratum pyramidale of CA3 | 0 | 17555 | 0 | 3443 | 941.2 |
| 80 | SGZ vs. polyform layer of dentate gyrus (cortex) | 0 | 17555 | 0 | 3464 | 946.3 |
| 60 | SGZ vs. stratum pyramidale of CA1 | 0 | 17555 | 0 | 3500 | 957.1 |
| 58 | SGZ vs. stratum pyramidale of CA2 | 0 | 17555 | 0 | 3594 | 983.3 |
| 101 | SGZ vs. subiculum | 0 | 17555 | 0 | 3746 | 1024 |
| 74 | SGZ vs. lateral nucleus | 0 | 17555 | 0 | 4555 | 1236 |
| 104 | SGZ vs. rostral periamygdaloid cortex (rPAC) | 0 | 17555 | 0 | 4602 | 1249 |
| 44 | SGZ vs. paralaminar nucleus | 0 | 17555 | 0 | 4610 | 1251 |
| 103 | SGZ vs. amygdalopiriform transition area | 0 | 17555 | 0 | 4747 | 1287 |
| 106 | SGZ vs. amygdalohippocampal area | 0 | 17555 | 0 | 4823 | 1310 |
| 43 | SGZ vs. stratum radiatum of CA1 | 0 | 17555 | 0 | 4840 | 1326 |
| 46 | SGZ vs. accessory basal nucleus (basomedial nucleus) | 0 | 17555 | 0 | 4890 | 1328 |
| 65 | SGZ vs. medial nucleus | 0 | 17555 | 0 | 4994 | 1353 |
| 105 | SGZ vs. anterior amygdaloid area | 0 | 17555 | 0 | 5193 | 1410 |
| 47 | SGZ vs. basal nucleus (basolateral nucleus) | 0 | 17555 | 0 | 5385 | 1463 |
| 107 | SGZ vs. central amygdaloid nucleus | 0 | 17555 | 0 | 5398 | 1468 |
| 90 | SGZ vs. layer II of V1 | 0 | 17555 | 0 | 5675 | 1584 |
| 89 | SGZ vs. layer III of V1 | 0 | 17555 | 0 | 5745 | 1602 |
| 98 | SGZ vs. layer IVA of V1 | 0 | 17555 | 0 | 5798 | 1618 |
| 110 | SGZ vs. layer II of V2 | 0 | 17555 | 0 | 5895 | 1644 |
| 114 | SGZ vs. layer III of V2 | 0 | 17555 | 0 | 5918 | 1650 |
| 92 | SGZ vs. layer II of rostral cingulate cortex | 0 | 17555 | 0 | 5942 | 1659 |
| 68 | SGZ vs. layer V of V1 | 0 | 17555 | 0 | 5955 | 1663 |
| 66 | SGZ vs. layer V of rostral cingulate cortex | 0 | 17555 | 0 | 5969 | 1665 |
| 112 | SGZ vs. layer V of V2 | 0 | 17555 | 0 | 6009 | 1676 |
| 122 | SGZ vs. layer V of caudal orbitofrontal cortex | 0 | 17555 | 0 | 6019 | 1680 |
| 113 | SGZ vs. layer VI of V2 | 0 | 17555 | 0 | 6032 | 1683 |
| 124 | SGZ vs. layer II of dorsolateral prefrontal cortex | 0 | 17555 | 0 | 6035 | 1683 |
| 100 | SGZ vs. olfactory tubercle | 0 | 17555 | 0 | 6037 | 1689 |
| 95 | SGZ vs. layer IVCa of V1 | 0 | 17555 | 0 | 6104 | 1705 |
| 120 | SGZ vs. layer II of caudal orbitofrontal cortex | 0 | 17555 | 0 | 6107 | 1705 |
| 121 | SGZ vs. layer III of caudal orbitofrontal cortex | 0 | 17555 | 0 | 6132 | 1713 |
| 51 | SGZ vs. layer VI of rostral cingulate cortex | 0 | 17555 | 0 | 6139 | 1710 |
| 127 | SGZ vs. layer V of dorsolateral prefrontal cortex | 0 | 17555 | 0 | 6144 | 1715 |
| 96 | SGZ vs. layer IVB of V1 | 0 | 17555 | 0 | 6159 | 1719 |
| 111 | SGZ vs. granular layer IV of V2 | 0 | 17555 | 0 | 6166 | 1720 |
| 125 | SGZ vs. layer III of dorsolateral prefrontal cortex | 0 | 17555 | 0 | 6168 | 1722 |
| 71 | SGZ vs. layer VI of V1 | 0 | 17555 | 0 | 6185 | 1729 |
| 126 | SGZ vs. granular layer IV of dorsolateral prefrontal cortex | 0 | 17555 | 0 | 6189 | 1728 |
| 108 | SGZ vs. layer I of V1 | 0 | 17555 | 0 | 6191 | 1727 |
| 117 | SGZ vs. granular layer IV of medial orbitofrontal cortex | 0 | 17555 | 0 | 6237 | 1743 |
| 9 | SGZ vs. putamen | 0 | 17555 | 0 | 6271 | 1754 |
| 93 | SGZ vs. layer III of rostral cingulate cortex | 0 | 17555 | 0 | 6303 | 1761 |
| 99 | SGZ vs. islands of Calleja | 0 | 17555 | 0 | 6309 | 1765 |
| 128 | SGZ vs. layer VI of dorsolateral prefrontal cortex | 0 | 17555 | 0 | 6310 | 1761 |
| 94 | SGZ vs. layer IVCb of V1 | 0 | 17555 | 0 | 6320 | 1766 |
| 123 | SGZ vs. layer VI of caudal orbitofrontal cortex | 0 | 17555 | 0 | 6413 | 1793 |
| 116 | SGZ vs. layer III of medial orbitofrontal cortex | 0 | 17555 | 0 | 6421 | 1796 |
| 118 | SGZ vs. layer V of medial orbitofrontal cortex | 0 | 17555 | 0 | 6429 | 1800 |
| 119 | SGZ vs. layer VI of medial orbitofrontal cortex | 0 | 17555 | 0 | 6525 | 1824 |
| 115 | SGZ vs. layer II of medial orbitofrontal cortex | 0 | 17555 | 0 | 6592 | 1848 |
| 31 | SGZ vs. external segment of globus pallidus | 0 | 17555 | 0 | 7077 | 1982 |
| 30 | SGZ vs. internal segment of globus pallidus | 0 | 17555 | 0 | 7728 | 2168 |
| 33 | SGZ vs. internal capsule | 0 | 17555 | 0 | 7758 | 2176 |
| 109 | SGZ vs. white matter of V1 | 0 | 17555 | 0 | 8284 | 2322 |
| 102 | SGZ vs. CA4 region | 0 | 17555 | 0 | 27339 | 6825 |
| 91 | SGZ vs. stratum oriens of CA1 | 0 | 17555 | 0 | 27349 | 6833 |
| 10 | SGZ vs. nucleus accumbens | 0 | 17555 | 0 | 29612 | 7546 |
| 3 | SGZ vs. caudate nucleus | 0 | 17555 | 0 | 29669 | 7567 |
| 1 | SGZ vs. ventricular zone of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 2 | SGZ vs. caudal ganglionic eminence | 17555 | 17555 | 100 | 136550 | 1320635 |
| 4 | SGZ vs. globus pallidus | 17555 | 17555 | 100 | 136550 | 1320635 |
| 5 | SGZ vs. cortical hem | 17555 | 17555 | 100 | 136550 | 1320635 |
| 6 | SGZ vs. medial ganglionic eminence | 17555 | 17555 | 100 | 136550 | 1320635 |
| 7 | SGZ vs. lateral ganglionic eminence | 17555 | 17555 | 100 | 136550 | 1320635 |
| 8 | SGZ vs. hippocampal subventricular zone of CA1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 11 | SGZ vs. inner ventricular zone of rostral cingulate cortex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 12 | SGZ vs. outer ventricular zone of rostral cingulate cortex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 13 | SGZ vs. subventricular zone of rostral cingulate cortex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 14 | SGZ vs. marginal zone of rostral cingulate cortex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 15 | SGZ vs. hippocampal ventricular zone of CA1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 16 | SGZ vs. subventricular zone of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 17 | SGZ vs. marginal zone of CA1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 18 | SGZ vs. amygdaloid complex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 19 | SGZ vs. inner ventricular zone of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 20 | SGZ vs. outer ventricular zone of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 21 | SGZ vs. inner cortical plate (infragranular layer) of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 22 | SGZ vs. marginal zone of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 23 | SGZ vs. intermediate zone of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 24 | SGZ vs. subplate zone of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 25 | SGZ vs. dorsal lateral geniculate nucleus | 17555 | 17555 | 100 | 136550 | 1320635 |
| 26 | SGZ vs. dentate migratory stream | 17555 | 17555 | 100 | 136550 | 1320635 |
| 27 | SGZ vs. hippocampal subplate of CA1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 28 | SGZ vs. hippocampal plate of CA1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 29 | SGZ vs. granular layer anlage of dentate gyrus (cortex) | 17555 | 17555 | 100 | 136550 | 1320635 |
| 32 | SGZ vs. lateral ganglionic eminence-cortex border | 17555 | 17555 | 100 | 136550 | 1320635 |
| 34 | SGZ vs. outer fiber (plexiform) zone of rostral cingulate cortex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 35 | SGZ vs. inner ventricular zone of S1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 36 | SGZ vs. outer ventricular zone of S1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 37 | SGZ vs. subventricular zone of S1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 38 | SGZ vs. subplate zone of S1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 39 | SGZ vs. inner cortical plate (infragranular layer) of S1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 40 | SGZ vs. intermediate zone of rostral cingulate cortex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 41 | SGZ vs. subplate zone of rostral cingulate cortex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 42 | SGZ vs. inner cortical plate (infragranular layer) of rostral cingulate cortex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 45 | SGZ vs. medial division of central nucleus | 17555 | 17555 | 100 | 136550 | 1320635 |
| 48 | SGZ vs. outer subventricular zone of rostral cingulate cortex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 49 | SGZ vs. inner fiber (plexiform) zone of rostral cingulate cortex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 50 | SGZ vs. outer cortical plate of rostral cingulate cortex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 52 | SGZ vs. outer fiber (plexiform) zone of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 53 | SGZ vs. inner subventricular zone of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 54 | SGZ vs. outer subventricular zone of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 55 | SGZ vs. intermediate cell dense zone of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 56 | SGZ vs. pyramidal layer of subiculum | 17555 | 17555 | 100 | 136550 | 1320635 |
| 57 | SGZ vs. hippocampal intermediate zone of CA1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 59 | SGZ vs. stratum lacunosum-moleculare of CA1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 61 | SGZ vs. inner subventricular zone of S1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 62 | SGZ vs. outer subventricular zone of S1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 63 | SGZ vs. layer VI of S1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 64 | SGZ vs. amygdaloid intramedullary gray | 17555 | 17555 | 100 | 136550 | 1320635 |
| 67 | SGZ vs. outer cortical plate of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 72 | SGZ vs. lateral division of central nucleus | 17555 | 17555 | 100 | 136550 | 1320635 |
| 75 | SGZ vs. transitory migratory zone of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 76 | SGZ vs. inner subventricular zone of rostral cingulate cortex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 77 | SGZ vs. cortical plate of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 78 | SGZ vs. ventricular zone of S1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 79 | SGZ vs. dorsal lateral geniculate nucleus, parvocellular layers | 17555 | 17555 | 100 | 136550 | 1320635 |
| 81 | SGZ vs. inner fiber (plexiform) zone of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 82 | SGZ vs. supragranular layer of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 83 | SGZ vs. granular layer IV of V1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 84 | SGZ vs. periamygdaloid cortex (cortical amygdaloid nucleus) | 17555 | 17555 | 100 | 136550 | 1320635 |
| 85 | SGZ vs. ventricular zone of rostral cingulate cortex | 17555 | 17555 | 100 | 136550 | 1320635 |
| 86 | SGZ vs. molecular layer of dentate gyrus (cortex) | 17555 | 17555 | 100 | 136550 | 1320635 |
| 87 | SGZ vs. dorsal lateral geniculate nucleus, magnocellular layers | 17555 | 17555 | 100 | 136550 | 1320635 |
| 88 | SGZ vs. outer cortical plate of S1 | 17555 | 17555 | 100 | 136550 | 1320635 |
| 97 | SGZ vs. dorsal lateral geniculate nucleus, koniocellular layers | 17555 | 17555 | 100 | 136550 | 1320635 |
#Subset just the candidate genes and re-run t-tests
## Dividing.Cell genes (Miller et al. 2013, Table S1)
gene.sub <- ex_mac[row.names(exprs(ex_mac)) %in% s1.genes, pData(ex_mac)$age=="48 mo"]
SGZ.samples <-exprs(gene.sub[,pData(gene.sub)$structure_acronym==c("DGsg")])
region_list <- as.character(unique(pData(gene.sub)$structure_name))
DGE_results <- NULL
DGE_summary <- data.frame()
for(region in region_list){
region.samples <-exprs( gene.sub[,pData(gene.sub)$structure_name==region] )
limma.data <-cbind(SGZ.samples, region.samples)
fit <-lmFit(limma.data, design=
c(rep(1,ncol(SGZ.samples)), rep(-1,ncol(region.samples))) )
fit <- eBayes(fit)
tab <- topTable(fit, number=Inf)
DGE_results[[paste("SGZ vs.",region)]] <- list(tab)
# Create summary table
sig <-dim(subset(tab, adj.P.Val<=0.05))[1]
total <- dim(tab)[1]
percent_DEGs <-round(dim(subset(tab, adj.P.Val<=0.05))[1] /
dim(tab)[1] *100,3)
DGE_summary <- rbind(DGE_summary,
data.frame(Test=paste("SGZ vs.", region), Sig_DEGs=sig,
Total_genes=total, Percent_DEGs=round(sig/total*100,3),
Total_logFC=sum(abs(tab$logFC)),
Total_t.stat=sum(abs(tab$t)))
)
}
# Print written summary
no_DEGs <-length(subset(DGE_summary, Sig_DEGs==0)$Sig_DEGs)
total_regions <-length(DGE_summary$Test)
paste(no_DEGs,"/",total_regions,"adult brain regions contained no Differentially Expressed Genes compared to adult SGZ.")
## [1] "61 / 61 adult brain regions contained no Differentially Expressed Genes compared to adult SGZ."
# Results table
dge <-subset(DGE_summary,Test!="<NA>")
pander(dge[order(c(dge$percent_DEGs, dge$Total_logFC)),], style='simple', justify='left', split.table=Inf)
| Test | Sig_DEGs | Total_genes | Percent_DEGs | Total_logFC | Total_t.stat | |
|---|---|---|---|---|---|---|
| 49 | SGZ vs. subgranular zone of dentate gyrus (cortex) | 0 | 250 | 0 | 1.327e-13 | 3.338e-14 |
| 44 | SGZ vs. granular layer of dentate gyrus (cortex) | 0 | 250 | 0 | 59.48 | 15.26 |
| 50 | SGZ vs. stratum pyramidale of CA3 | 0 | 250 | 0 | 75.55 | 18.69 |
| 46 | SGZ vs. stratum pyramidale of CA1 | 0 | 250 | 0 | 75.68 | 18.83 |
| 42 | SGZ vs. stratum pyramidale of CA2 | 0 | 250 | 0 | 77.22 | 19.13 |
| 3 | SGZ vs. layer V of rostral cingulate cortex | 0 | 250 | 0 | 93.68 | 23.53 |
| 45 | SGZ vs. subiculum | 0 | 250 | 0 | 94.2 | 23.06 |
| 52 | SGZ vs. paralaminar nucleus | 0 | 250 | 0 | 97.19 | 23.74 |
| 16 | SGZ vs. layer V of caudal orbitofrontal cortex | 0 | 250 | 0 | 97.84 | 24.51 |
| 8 | SGZ vs. layer V of dorsolateral prefrontal cortex | 0 | 250 | 0 | 98.73 | 24.8 |
| 17 | SGZ vs. layer VI of caudal orbitofrontal cortex | 0 | 250 | 0 | 99.15 | 24.84 |
| 18 | SGZ vs. layer VI of dorsolateral prefrontal cortex | 0 | 250 | 0 | 100.5 | 25.05 |
| 1 | SGZ vs. layer II of rostral cingulate cortex | 0 | 250 | 0 | 100.5 | 25.22 |
| 2 | SGZ vs. layer III of rostral cingulate cortex | 0 | 250 | 0 | 100.7 | 25.33 |
| 58 | SGZ vs. amygdalohippocampal area | 0 | 250 | 0 | 100.9 | 24.53 |
| 12 | SGZ vs. layer V of medial orbitofrontal cortex | 0 | 250 | 0 | 101.4 | 25.49 |
| 23 | SGZ vs. olfactory tubercle | 0 | 250 | 0 | 101.6 | 25.5 |
| 4 | SGZ vs. layer VI of rostral cingulate cortex | 0 | 250 | 0 | 102 | 25.34 |
| 11 | SGZ vs. granular layer IV of medial orbitofrontal cortex | 0 | 250 | 0 | 102.3 | 25.73 |
| 13 | SGZ vs. layer VI of medial orbitofrontal cortex | 0 | 250 | 0 | 102.3 | 25.48 |
| 15 | SGZ vs. layer III of caudal orbitofrontal cortex | 0 | 250 | 0 | 103 | 25.96 |
| 14 | SGZ vs. layer II of caudal orbitofrontal cortex | 0 | 250 | 0 | 103.7 | 25.97 |
| 54 | SGZ vs. lateral nucleus | 0 | 250 | 0 | 104.1 | 25.34 |
| 7 | SGZ vs. granular layer IV of dorsolateral prefrontal cortex | 0 | 250 | 0 | 104.6 | 26.27 |
| 55 | SGZ vs. amygdalopiriform transition area | 0 | 250 | 0 | 105.8 | 25.64 |
| 6 | SGZ vs. layer III of dorsolateral prefrontal cortex | 0 | 250 | 0 | 106.1 | 26.66 |
| 41 | SGZ vs. layer VI of V2 | 0 | 250 | 0 | 106.2 | 26.48 |
| 56 | SGZ vs. rostral periamygdaloid cortex (rPAC) | 0 | 250 | 0 | 106.4 | 25.76 |
| 10 | SGZ vs. layer III of medial orbitofrontal cortex | 0 | 250 | 0 | 106.4 | 26.76 |
| 9 | SGZ vs. layer II of medial orbitofrontal cortex | 0 | 250 | 0 | 106.7 | 26.84 |
| 57 | SGZ vs. accessory basal nucleus (basomedial nucleus) | 0 | 250 | 0 | 107.2 | 26.02 |
| 5 | SGZ vs. layer II of dorsolateral prefrontal cortex | 0 | 250 | 0 | 107.4 | 26.83 |
| 33 | SGZ vs. layer V of V1 | 0 | 250 | 0 | 107.7 | 26.9 |
| 39 | SGZ vs. layer V of V2 | 0 | 250 | 0 | 107.9 | 26.91 |
| 22 | SGZ vs. putamen | 0 | 250 | 0 | 108.2 | 27.21 |
| 47 | SGZ vs. stratum radiatum of CA1 | 0 | 250 | 0 | 109.4 | 26.62 |
| 34 | SGZ vs. layer VI of V1 | 0 | 250 | 0 | 109.5 | 27.42 |
| 43 | SGZ vs. polyform layer of dentate gyrus (cortex) | 0 | 250 | 0 | 109.7 | 26.49 |
| 27 | SGZ vs. layer II of V1 | 0 | 250 | 0 | 110.4 | 27.65 |
| 28 | SGZ vs. layer III of V1 | 0 | 250 | 0 | 110.6 | 27.7 |
| 36 | SGZ vs. layer II of V2 | 0 | 250 | 0 | 111.1 | 27.74 |
| 59 | SGZ vs. basal nucleus (basolateral nucleus) | 0 | 250 | 0 | 112.2 | 27.25 |
| 38 | SGZ vs. granular layer IV of V2 | 0 | 250 | 0 | 112.9 | 28.21 |
| 29 | SGZ vs. layer IVA of V1 | 0 | 250 | 0 | 113.6 | 28.46 |
| 37 | SGZ vs. layer III of V2 | 0 | 250 | 0 | 114 | 28.45 |
| 21 | SGZ vs. islands of Calleja | 0 | 250 | 0 | 114 | 28.49 |
| 60 | SGZ vs. central amygdaloid nucleus | 0 | 250 | 0 | 119.4 | 28.92 |
| 30 | SGZ vs. layer IVB of V1 | 0 | 250 | 0 | 119.8 | 29.9 |
| 32 | SGZ vs. layer IVCb of V1 | 0 | 250 | 0 | 120.5 | 30.24 |
| 53 | SGZ vs. medial nucleus | 0 | 250 | 0 | 122 | 29.5 |
| 31 | SGZ vs. layer IVCa of V1 | 0 | 250 | 0 | 122.7 | 30.83 |
| 26 | SGZ vs. layer I of V1 | 0 | 250 | 0 | 123.6 | 30.36 |
| 51 | SGZ vs. anterior amygdaloid area | 0 | 250 | 0 | 127.9 | 30.84 |
| 24 | SGZ vs. external segment of globus pallidus | 0 | 250 | 0 | 131.6 | 32.63 |
| 20 | SGZ vs. internal capsule | 0 | 250 | 0 | 135.9 | 34.08 |
| 25 | SGZ vs. internal segment of globus pallidus | 0 | 250 | 0 | 143.2 | 35.73 |
| 35 | SGZ vs. white matter of V1 | 0 | 250 | 0 | 153.4 | 38.4 |
| 48 | SGZ vs. stratum oriens of CA1 | 0 | 250 | 0 | 353.2 | 77.83 |
| 61 | SGZ vs. CA4 region | 0 | 250 | 0 | 396.4 | 89.38 |
| 40 | SGZ vs. nucleus accumbens | 0 | 250 | 0 | 417.1 | 95.1 |
| 19 | SGZ vs. caudate nucleus | 0 | 250 | 0 | 429.5 | 98.66 |
#Subset just the candidate genes and re-run t-tests
## Dividing.Cell genes (Miller et al. 2013, Table S4)
gene.sub <- ex_mac[row.names(exprs(ex_mac)) %in% Mac_mod.tan, pData(ex_mac)$age=="48 mo"]
SGZ.samples <-exprs(gene.sub[,pData(gene.sub)$structure_acronym==c("DGsg")])
region_list <- as.character(unique(pData(gene.sub)$structure_name))
DGE_results <- NULL
DGE_summary <- data.frame()
for(region in region_list){
region.samples <-exprs( gene.sub[,pData(gene.sub)$structure_name==region] )
limma.data <-cbind(SGZ.samples, region.samples)
fit <-lmFit(limma.data, design=
c(rep(1,ncol(SGZ.samples)), rep(-1,ncol(region.samples))) )
fit <- eBayes(fit)
tab <- topTable(fit, number=Inf)
DGE_results[[paste("SGZ vs.",region)]] <- list(tab)
# Create summary table
sig <-dim(subset(tab, adj.P.Val<=0.05))[1]
total <- dim(tab)[1]
percent_DEGs <-round(dim(subset(tab, adj.P.Val<=0.05))[1] /
dim(tab)[1] *100,3)
DGE_summary <- rbind(DGE_summary,
data.frame(Test=paste("SGZ vs.", region), Sig_DEGs=sig,
Total_genes=total, Percent_DEGs=round(sig/total*100,3),
Total_logFC=sum(abs(tab$logFC)),
Total_t.stat=sum(abs(tab$t)))
)
}
# Print written summary
no_DEGs <-length(subset(DGE_summary, Sig_DEGs==0)$Sig_DEGs)
total_regions <-length(DGE_summary$Test)
paste(no_DEGs,"/",total_regions,"adult brain regions contained no Differentially Expressed Genes compared to adult SGZ.")
## [1] "61 / 61 adult brain regions contained no Differentially Expressed Genes compared to adult SGZ."
# Results table
dge <-subset(DGE_summary,Test!="<NA>")
pander(dge[order(c(dge$percent_DEGs, dge$Total_logFC)),], style='simple', justify='left', split.table=Inf)
| Test | Sig_DEGs | Total_genes | Percent_DEGs | Total_logFC | Total_t.stat | |
|---|---|---|---|---|---|---|
| 49 | SGZ vs. subgranular zone of dentate gyrus (cortex) | 0 | 159 | 0 | 8.449e-14 | 2.223e-14 |
| 44 | SGZ vs. granular layer of dentate gyrus (cortex) | 0 | 159 | 0 | 24.2 | 6.45 |
| 50 | SGZ vs. stratum pyramidale of CA3 | 0 | 159 | 0 | 44.14 | 11.54 |
| 58 | SGZ vs. amygdalohippocampal area | 0 | 159 | 0 | 45.72 | 11.87 |
| 56 | SGZ vs. rostral periamygdaloid cortex (rPAC) | 0 | 159 | 0 | 46.88 | 12.15 |
| 42 | SGZ vs. stratum pyramidale of CA2 | 0 | 159 | 0 | 47.34 | 12.39 |
| 55 | SGZ vs. amygdalopiriform transition area | 0 | 159 | 0 | 47.79 | 12.37 |
| 46 | SGZ vs. stratum pyramidale of CA1 | 0 | 159 | 0 | 48.55 | 12.72 |
| 54 | SGZ vs. lateral nucleus | 0 | 159 | 0 | 49.34 | 12.8 |
| 57 | SGZ vs. accessory basal nucleus (basomedial nucleus) | 0 | 159 | 0 | 49.57 | 12.87 |
| 53 | SGZ vs. medial nucleus | 0 | 159 | 0 | 52.54 | 13.55 |
| 45 | SGZ vs. subiculum | 0 | 159 | 0 | 52.66 | 13.68 |
| 26 | SGZ vs. layer I of V1 | 0 | 159 | 0 | 53.09 | 13.92 |
| 23 | SGZ vs. olfactory tubercle | 0 | 159 | 0 | 53.83 | 14.3 |
| 22 | SGZ vs. putamen | 0 | 159 | 0 | 54.07 | 14.38 |
| 43 | SGZ vs. polyform layer of dentate gyrus (cortex) | 0 | 159 | 0 | 55 | 14.06 |
| 21 | SGZ vs. islands of Calleja | 0 | 159 | 0 | 55.16 | 14.58 |
| 14 | SGZ vs. layer II of caudal orbitofrontal cortex | 0 | 159 | 0 | 55.4 | 14.79 |
| 52 | SGZ vs. paralaminar nucleus | 0 | 159 | 0 | 55.69 | 14.34 |
| 5 | SGZ vs. layer II of dorsolateral prefrontal cortex | 0 | 159 | 0 | 56.15 | 14.94 |
| 1 | SGZ vs. layer II of rostral cingulate cortex | 0 | 159 | 0 | 56.49 | 15.11 |
| 51 | SGZ vs. anterior amygdaloid area | 0 | 159 | 0 | 57.02 | 14.68 |
| 59 | SGZ vs. basal nucleus (basolateral nucleus) | 0 | 159 | 0 | 57.44 | 14.89 |
| 47 | SGZ vs. stratum radiatum of CA1 | 0 | 159 | 0 | 58.26 | 14.99 |
| 60 | SGZ vs. central amygdaloid nucleus | 0 | 159 | 0 | 58.29 | 15.02 |
| 36 | SGZ vs. layer II of V2 | 0 | 159 | 0 | 58.35 | 15.56 |
| 9 | SGZ vs. layer II of medial orbitofrontal cortex | 0 | 159 | 0 | 58.61 | 15.7 |
| 27 | SGZ vs. layer II of V1 | 0 | 159 | 0 | 58.88 | 15.73 |
| 4 | SGZ vs. layer VI of rostral cingulate cortex | 0 | 159 | 0 | 59.43 | 15.61 |
| 3 | SGZ vs. layer V of rostral cingulate cortex | 0 | 159 | 0 | 60.32 | 16.12 |
| 13 | SGZ vs. layer VI of medial orbitofrontal cortex | 0 | 159 | 0 | 60.78 | 16.03 |
| 10 | SGZ vs. layer III of medial orbitofrontal cortex | 0 | 159 | 0 | 61.23 | 16.41 |
| 2 | SGZ vs. layer III of rostral cingulate cortex | 0 | 159 | 0 | 61.98 | 16.63 |
| 12 | SGZ vs. layer V of medial orbitofrontal cortex | 0 | 159 | 0 | 62.69 | 16.76 |
| 28 | SGZ vs. layer III of V1 | 0 | 159 | 0 | 62.84 | 16.78 |
| 11 | SGZ vs. granular layer IV of medial orbitofrontal cortex | 0 | 159 | 0 | 63.19 | 16.9 |
| 18 | SGZ vs. layer VI of dorsolateral prefrontal cortex | 0 | 159 | 0 | 63.24 | 16.66 |
| 17 | SGZ vs. layer VI of caudal orbitofrontal cortex | 0 | 159 | 0 | 63.36 | 16.83 |
| 33 | SGZ vs. layer V of V1 | 0 | 159 | 0 | 63.39 | 16.84 |
| 39 | SGZ vs. layer V of V2 | 0 | 159 | 0 | 63.48 | 16.84 |
| 32 | SGZ vs. layer IVCb of V1 | 0 | 159 | 0 | 63.72 | 16.92 |
| 37 | SGZ vs. layer III of V2 | 0 | 159 | 0 | 64.09 | 17.09 |
| 41 | SGZ vs. layer VI of V2 | 0 | 159 | 0 | 64.13 | 16.92 |
| 16 | SGZ vs. layer V of caudal orbitofrontal cortex | 0 | 159 | 0 | 64.13 | 17.08 |
| 15 | SGZ vs. layer III of caudal orbitofrontal cortex | 0 | 159 | 0 | 64.33 | 17.31 |
| 29 | SGZ vs. layer IVA of V1 | 0 | 159 | 0 | 64.35 | 17.15 |
| 8 | SGZ vs. layer V of dorsolateral prefrontal cortex | 0 | 159 | 0 | 65.17 | 17.39 |
| 6 | SGZ vs. layer III of dorsolateral prefrontal cortex | 0 | 159 | 0 | 65.35 | 17.52 |
| 7 | SGZ vs. granular layer IV of dorsolateral prefrontal cortex | 0 | 159 | 0 | 65.45 | 17.48 |
| 31 | SGZ vs. layer IVCa of V1 | 0 | 159 | 0 | 66 | 17.55 |
| 30 | SGZ vs. layer IVB of V1 | 0 | 159 | 0 | 66.17 | 17.57 |
| 38 | SGZ vs. granular layer IV of V2 | 0 | 159 | 0 | 66.18 | 17.58 |
| 34 | SGZ vs. layer VI of V1 | 0 | 159 | 0 | 66.39 | 17.62 |
| 24 | SGZ vs. external segment of globus pallidus | 0 | 159 | 0 | 67.85 | 17.82 |
| 25 | SGZ vs. internal segment of globus pallidus | 0 | 159 | 0 | 75.87 | 19.96 |
| 20 | SGZ vs. internal capsule | 0 | 159 | 0 | 76.19 | 19.98 |
| 35 | SGZ vs. white matter of V1 | 0 | 159 | 0 | 81.26 | 21.1 |
| 48 | SGZ vs. stratum oriens of CA1 | 0 | 159 | 0 | 231.5 | 54.13 |
| 61 | SGZ vs. CA4 region | 0 | 159 | 0 | 253 | 60.37 |
| 40 | SGZ vs. nucleus accumbens | 0 | 159 | 0 | 264.1 | 63.77 |
| 19 | SGZ vs. caudate nucleus | 0 | 159 | 0 | 269.1 | 65.27 |
Attempt to recreate networks from Miller et al. 2013
library(WGCNA)
## ==========================================================================
## *
## * Package WGCNA 1.61 loaded.
## *
## * Important note: It appears that your system supports multi-threading,
## * but it is not enabled within WGCNA in R.
## * To allow multi-threading within WGCNA with all available cores, use
## *
## * allowWGCNAThreads()
## *
## * within R. Use disableWGCNAThreads() to disable threading if necessary.
## * Alternatively, set the following environment variable on your system:
## *
## * ALLOW_WGCNA_THREADS=<number_of_processors>
## *
## * for example
## *
## * ALLOW_WGCNA_THREADS=8
## *
## * To set the environment variable in linux bash shell, type
## *
## * export ALLOW_WGCNA_THREADS=8
## *
## * before running R. Other operating systems or shells will
## * have a similar command to achieve the same aim.
## *
## ==========================================================================
library(flashClust)
allowWGCNAThreads() # Enable multi-threading
## Allowing multi-threading with up to 8 threads.
all.SGZ.GCL <- ex_mac[,pData(ex_mac)$structure_acronym %in% c("DGsg","DGgr")]
datExpr <- exprs(all.SGZ.GCL)
#net_corrs <- function(eset1, eset2){
## Soft threshold
powers = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14)
sft = pickSoftThreshold(datExpr, networkType="signed", powerVector=powers) # t(net$MEs)
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.205 58.80 0.30000 48.4 48.4 48.8
## 2 2 0.215 30.10 0.32600 46.9 46.9 47.7
## 3 3 0.232 21.10 0.34000 45.4 45.5 46.6
## 4 4 0.258 17.30 0.36700 44.0 44.1 45.5
## 5 5 0.422 15.00 0.66000 42.7 42.8 44.5
## 6 6 0.427 12.70 0.65900 41.4 41.5 43.5
## 7 7 0.433 11.00 0.65000 40.2 40.3 42.5
## 8 8 0.438 9.74 0.64800 39.0 39.2 41.5
## 9 9 0.455 8.90 0.64900 37.8 38.1 40.6
## 10 10 0.462 8.18 0.63200 36.7 37.0 39.7
## 11 11 0.466 7.51 0.62900 35.7 36.0 38.8
## 12 12 0.477 7.12 0.60100 34.7 35.0 37.9
## 13 13 0.607 7.99 0.65800 33.7 34.0 37.1
## 14 14 0.219 31.60 0.00827 32.8 33.1 36.2
### Plot powers
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n",
main = paste("Scale independence"))
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
labels=powers,cex=0.9,col="red");
# this line corresponds to using an R^2 cut-off of h
#abline(h=0.9,col="red")
## Run
POWER=14 # Same as Miller et al. 2013
# Assume defaults for everything not mentioned in Miller et al. 2013
#net.mac.SGZ_GCL = blockwiseModules(t(datExpr), power=POWER, networkType="signed")
#save(net.mac.SGZ_GCL, file="WGCNA_ABA.macaque_SGZ.GCL.rda")
#___________________________________________________________________________________#
# Plot WGCNA results
load("~/Desktop/Research/Signatures_of_Neurogensis/WGCNA_ABA.macaque_SGZ.GCL.rda")
# Plot
num_mods <- length(colnames(net.mac.SGZ_GCL$MEs))
moduleColorsAutomatic = WGCNA::labels2colors(net.mac.SGZ_GCL$colors)
mColors = moduleColorsAutomatic[net.mac.SGZ_GCL$blockGenes[[1]]]
plotDendroAndColors(net.mac.SGZ_GCL$dendrograms[[1]], colors=mColors, dendroLabels=F,
groupLabels=c(paste("deepSplit = 2\n# modules =",num_mods)), addGuide=T,main="WGCNA Modules:\n Macaque SGZ & GCL samples")
# Get mutually exclusive modules assignments for each gene
mod_assignments <-data.frame(ModuleColors=net.mac.SGZ_GCL$colors, genes=row.names(datExpr))
mod_assignments <- mod_assignments[order(mod_assignments$ModuleColors),]
write.csv(mod_assignments, "module.gene.assignments_SGZ.GCL.csv")
modules <-unique(mod_assignments$ModuleColors)
module.overlap_summary <-data.frame()
for(color in modules){
one.module <-subset(mod_assignments,ModuleColors==color)
percent_tan.overlap <-round(sum(Mac_mod.tan %in% as.character(one.module$genes)) / length(one.module$genes)*100,3)
module.overlap_summary <- rbind(module.overlap_summary,data.frame(New_Module=color, New_Module_Size=length(one.module$genes), Percent_chance.overlap=length(one.module$genes)/dim(exprs(ex_mac))[1]*100, Percent_Tan.overlap=percent_tan.overlap))
}
pander::pander(module.overlap_summary[order(-module.overlap_summary$Percent_Tan.overlap),],style='simple',justify='left', split.table=Inf)
| New_Module | New_Module_Size | Percent_chance.overlap | Percent_Tan.overlap | |
|---|---|---|---|---|
| 19 | magenta | 476 | 2.711 | 13.87 |
| 32 | sienna3 | 38 | 0.2165 | 7.895 |
| 7 | darkmagenta | 51 | 0.2905 | 7.843 |
| 15 | grey60 | 146 | 0.8317 | 2.74 |
| 4 | cyan | 243 | 1.384 | 2.469 |
| 12 | green | 1021 | 5.816 | 2.449 |
| 22 | orange | 87 | 0.4956 | 2.299 |
| 17 | lightgreen | 142 | 0.8089 | 2.113 |
| 31 | salmon | 301 | 1.715 | 1.993 |
| 30 | saddlebrown | 67 | 0.3817 | 1.493 |
| 33 | skyblue | 67 | 0.3817 | 1.493 |
| 5 | darkgreen | 97 | 0.5525 | 1.031 |
| 13 | greenyellow | 389 | 2.216 | 0.771 |
| 14 | grey | 285 | 1.623 | 0.702 |
| 36 | tan | 340 | 1.937 | 0.588 |
| 27 | purple | 406 | 2.313 | 0.493 |
| 3 | brown | 1110 | 6.323 | 0.45 |
| 1 | black | 742 | 4.227 | 0.404 |
| 2 | blue | 1237 | 7.046 | 0.404 |
| 28 | red | 1014 | 5.776 | 0.296 |
| 40 | yellow | 1058 | 6.027 | 0.189 |
| 37 | turquoise | 6166 | 35.12 | 0.162 |
| 6 | darkgrey | 87 | 0.4956 | 0 |
| 8 | darkolivegreen | 51 | 0.2905 | 0 |
| 9 | darkorange | 81 | 0.4614 | 0 |
| 10 | darkred | 115 | 0.6551 | 0 |
| 11 | darkturquoise | 94 | 0.5355 | 0 |
| 16 | lightcyan | 157 | 0.8943 | 0 |
| 18 | lightyellow | 134 | 0.7633 | 0 |
| 20 | mediumpurple3 | 23 | 0.131 | 0 |
| 21 | midnightblue | 231 | 1.316 | 0 |
| 23 | orangered4 | 30 | 0.1709 | 0 |
| 24 | paleturquoise | 65 | 0.3703 | 0 |
| 25 | pink | 575 | 3.275 | 0 |
| 26 | plum1 | 31 | 0.1766 | 0 |
| 29 | royalblue | 128 | 0.7291 | 0 |
| 34 | skyblue3 | 34 | 0.1937 | 0 |
| 35 | steelblue | 66 | 0.376 | 0 |
| 38 | violet | 57 | 0.3247 | 0 |
| 39 | white | 77 | 0.4386 | 0 |
| 41 | yellowgreen | 36 | 0.2051 | 0 |
Create new networks using all samples
datExpr <- exprs(ex_mac)
#net_corrs <- function(eset1, eset2){
library(WGCNA)
library(flashClust)
## Soft threshold
powers = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14)
sft = pickSoftThreshold(datExpr, networkType="signed", powerVector=powers) # t(net$MEs)
## Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
## 1 1 0.819 99.40 0.922 1770 1770 1790
## 2 2 0.820 50.20 0.939 1690 1690 1740
## 3 3 0.816 33.40 0.936 1610 1620 1680
## 4 4 0.811 25.00 0.934 1540 1550 1630
## 5 5 0.810 19.70 0.939 1470 1480 1570
## 6 6 0.801 16.80 0.945 1400 1420 1520
## 7 7 0.796 14.40 0.944 1340 1360 1470
## 8 8 0.793 12.60 0.944 1280 1300 1430
## 9 9 0.783 11.10 0.937 1230 1250 1380
## 10 10 0.786 10.50 0.945 1180 1200 1340
## 11 11 0.783 9.49 0.946 1130 1150 1300
## 12 12 0.776 8.62 0.944 1080 1100 1260
## 13 13 0.773 7.92 0.942 1030 1060 1220
## 14 14 0.769 7.33 0.941 991 1020 1180
### Plot powers
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n",
main = paste("Scale independence"))
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
labels=powers,cex=0.9,col="red");
# this line corresponds to using an R^2 cut-off of h
#abline(h=0.9,col="red")
## Run
POWER=14 # Same as Miller et al. 2013
# Assume defaults for everything not mentioned in Miller et al. 2013
#net.mac = blockwiseModules(t(datExpr), power=POWER, networkType="signed", deepSplit=2)
# save(net.mac, file="WGCNA_ABA.macaque_all.samples.rda")
#___________________________________________________________________________________#
# Plot WGCNA results
load("~/Desktop/Research/Signatures_of_Neurogensis/WGCNA_ABA.macaque_all.samples.rda")
# Plot
num_mods <- length(colnames(net.mac$MEs))
moduleColorsAutomatic = WGCNA::labels2colors(net.mac$colors)
mColors = moduleColorsAutomatic[net.mac$blockGenes[[1]]]
plotDendroAndColors(net.mac$dendrograms[[1]], colors=mColors, dendroLabels=F,
groupLabels=c(paste("deepSplit = 2\n# modules =",num_mods)), addGuide=T,main="Cluster Dendrogram")
# Get mutually exclusive modules assignments for each gene
mod_assignments <-data.frame(ModuleColors=net.mac$colors, genes=row.names(datExpr))
mod_assignments <-mod_assignments[order(mod_assignments$ModuleColors),]
write.csv(mod_assignments, "module.gene.assignments_all.regions.csv")
modules <-unique(mod_assignments$ModuleColors)
module.overlap_summary <-data.frame()
for(color in modules){
one.module <-subset(mod_assignments,ModuleColors==color)
percent_tan.overlap <-round(sum(Mac_mod.tan %in% as.character(one.module$genes)) / length(one.module$genes)*100,3)
module.overlap_summary <- rbind(module.overlap_summary,data.frame(New_Module=color, New_Module_Size=length(one.module$genes), Percent_chance.overlap=length(one.module$genes)/dim(exprs(ex_mac))[1]*100, Percent_Tan.overlap=percent_tan.overlap))
}
pander::pander(module.overlap_summary[order(-module.overlap_summary$Percent_Tan.overlap),],style='simple',justify='left', split.table=Inf)
| New_Module | New_Module_Size | Percent_chance.overlap | Percent_Tan.overlap | |
|---|---|---|---|---|
| 12 | green | 997 | 5.679 | 4.714 |
| 27 | red | 656 | 3.737 | 4.116 |
| 22 | orangered4 | 41 | 0.2336 | 2.439 |
| 18 | lightyellow | 232 | 1.322 | 2.155 |
| 10 | darkred | 196 | 1.116 | 2.041 |
| 14 | grey | 493 | 2.808 | 2.028 |
| 8 | darkolivegreen | 108 | 0.6152 | 1.852 |
| 13 | greenyellow | 378 | 2.153 | 1.852 |
| 21 | orange | 169 | 0.9627 | 1.775 |
| 23 | paleturquoise | 140 | 0.7975 | 1.429 |
| 34 | steelblue | 141 | 0.8032 | 1.418 |
| 25 | plum1 | 75 | 0.4272 | 1.333 |
| 38 | white | 164 | 0.9342 | 1.22 |
| 16 | lightcyan | 252 | 1.435 | 1.19 |
| 39 | yellow | 1059 | 6.032 | 0.944 |
| 19 | magenta | 435 | 2.478 | 0.92 |
| 37 | violet | 136 | 0.7747 | 0.735 |
| 9 | darkorange | 168 | 0.957 | 0.595 |
| 6 | darkgrey | 174 | 0.9912 | 0.575 |
| 1 | black | 609 | 3.469 | 0.493 |
| 15 | grey60 | 250 | 1.424 | 0.4 |
| 24 | pink | 519 | 2.956 | 0.385 |
| 20 | midnightblue | 272 | 1.549 | 0.368 |
| 4 | cyan | 289 | 1.646 | 0.346 |
| 36 | turquoise | 3413 | 19.44 | 0.293 |
| 3 | brown | 1450 | 8.26 | 0.276 |
| 2 | blue | 2188 | 12.46 | 0.183 |
| 5 | darkgreen | 192 | 1.094 | 0 |
| 7 | darkmagenta | 99 | 0.5639 | 0 |
| 11 | darkturquoise | 176 | 1.003 | 0 |
| 17 | lightgreen | 244 | 1.39 | 0 |
| 26 | purple | 409 | 2.33 | 0 |
| 28 | royalblue | 227 | 1.293 | 0 |
| 29 | saddlebrown | 159 | 0.9057 | 0 |
| 30 | salmon | 293 | 1.669 | 0 |
| 31 | sienna3 | 90 | 0.5127 | 0 |
| 32 | skyblue | 162 | 0.9228 | 0 |
| 33 | skyblue3 | 82 | 0.4671 | 0 |
| 35 | tan | 329 | 1.874 | 0 |
| 40 | yellowgreen | 89 | 0.507 | 0 |
# Subset all samples from SGZ or GCL
all.SGZ.GCL <- ex_mac[,pData(ex_mac)$structure_acronym %in% c("DGsg","DGgr")]
# Setup up inputs
multiExpr = list(A1=list(data=t(exprs(ex_mac))), A2=list(data=t(exprs(all.SGZ.GCL))) )
multiColor = list(A1 = net.mac$colors)
# Run network preservation
mp <- modulePreservation(multiExpr, multiColor, referenceNetworks=1, verbose=3, networkType="signed", nPermutations=30, maxGoldModuleSize=100)
save(mp, file="network.preservation_all.samples.vs.GCL.SGZ.rda")
load("~/Desktop/Research/Signatures_of_Neurogensis/network.preservation_all.samples.vs.GCL.SGZ.rda")
# Summary stats
stats <- mp$preservation$Z$ref.A1$inColumnsAlsoPresentIn.A2 # All stats output
pander::pander(stats[order(-stats[,2]),c(1:2)][1:10,],style='simple',justify='left')
| moduleSize | Zsummary.pres | |
|---|---|---|
| yellow | 1000 | 40.12 |
| turquoise | 1000 | 29.84 |
| brown | 1000 | 26.32 |
| magenta | 326 | 25.12 |
| green | 770 | 22.88 |
| red | 435 | 21.55 |
| lightcyan | 179 | 19.75 |
| lightyellow | 162 | 18.31 |
| grey60 | 173 | 16.48 |
| lightgreen | 163 | 15.11 |
# Plot summary stats (Zsummary.pres)
library(ggplot2)
ggplot(stats, aes(y=Zsummary.pres, x=factor(rownames(stats)),
fill=factor(rownames(stats))) ) +
geom_bar(stat="identity") + labs(title=paste("Network preservation scores"), y="Zsummary.pres", x="Network module") + theme(legend.position="none",axis.text.x = element_text(angle=45, hjust=1)) + scale_fill_manual(values=rownames(stats))
# [Method 1]
gse2 <- getGEO("GSE71485", GSEMatrix=T) # imports 0 features...
#show(gse2)
# [Method 2]
library(GEOquery)
filePaths <-getGEOSuppFiles("GSE71485")
gse <-read.table(row.names(filePaths))
library(Biobase)
Biobase::exprs(gse2) <-data.frame(gse2)
gse.eset <-ExpressionSet(gse2, annotation ="hgu133plus2.db" )
library(EWCE)
Shin <- read_celltype_data("~/Desktop/Research/Signatures_of_Neurogensis/scRNAseq_datasets/GSE71485_Single_TPM.txt")
library(annotate)
# Create eset
ex_mac <- ExpressionSet(datExpr.mac)
# Create macaque phenoData:
sampleInfo.mac$unique_id <- factor(paste(sampleInfo.mac$donor_name, sampleInfo.mac$structure_acronym, sampleInfo.mac$well_id, sep="_"))
p <- data.frame(sampleInfo.mac)
rownames(p) <- sampleInfo.mac$unique_id
pdata <- AnnotatedDataFrame(p)
# Replace phenoData in mac ExpressionSet
phenoData(ex_mac) <- pdata
# Insert condition
ex_mac$treatment <- "Macaque"
ex_mac <-ex_mac[,colSums(is.na(exprs(ex_mac))) == 0] # Remove cols with NA
# Set up gene list
example_genelist <- S1$MouseGene
library("EWCE")
load(file="~/Desktop/Research/Deconvolution_of_Evolution/celltype_data.rda")
# Find mouse homologues of human genes
data("mouse_to_human_homologs")
m2h = unique(mouse_to_human_homologs[,c("HGNC.symbol","MGI.symbol")])
mouse.hits = unique(m2h[m2h$MGI.symbol %in% example_genelist,"MGI.symbol"])
mouse.bg = unique(setdiff(m2h$MGI.symbol,mouse.hits))
paste(length(mouse.hits),"/",length(example_genelist),"candidate genes found in sc-RNA-seq reference database.")
## [1] "341 / 363 candidate genes found in sc-RNA-seq reference database."
# Bootstrap significance testing:
## 1) Without controlling for transcript length and GC content
full_results.sub = bootstrap.enrichment.test(sct_data=celltype_data,
mouse.hits=mouse.hits,mouse.bg=mouse.bg,
reps=10000, sub=T)
## [1] "interneurons_Int10"
## [1] 0
## [1] "Fold enrichment: 2.19482408258267"
## [1] "Standard deviations from mean: 14.0130691674156"
## [1] ""
## [1] "interneurons_Int6"
## [1] 0
## [1] "Fold enrichment: 1.85257520115396"
## [1] "Standard deviations from mean: 11.2158270271469"
## [1] ""
## [1] "interneurons_Int9"
## [1] 0
## [1] "Fold enrichment: 1.95878786651468"
## [1] "Standard deviations from mean: 9.54077432172045"
## [1] ""
## [1] "interneurons_Int2"
## [1] 1e-04
## [1] "Fold enrichment: 1.5275445211573"
## [1] "Standard deviations from mean: 7.04677513191367"
## [1] ""
## [1] "interneurons_Int4"
## [1] 0.006
## [1] "Fold enrichment: 1.2205531572426"
## [1] "Standard deviations from mean: 2.85248747675033"
## [1] ""
## [1] "interneurons_Int1"
## [1] 0
## [1] "Fold enrichment: 1.81674537423394"
## [1] "Standard deviations from mean: 8.71008017983052"
## [1] ""
## [1] "interneurons_Int3"
## [1] 0
## [1] "Fold enrichment: 2.17136793226558"
## [1] "Standard deviations from mean: 12.5208453228451"
## [1] ""
## [1] "interneurons_Int13"
## [1] 0
## [1] "Fold enrichment: 1.8108479968661"
## [1] "Standard deviations from mean: 9.96500593937373"
## [1] ""
## [1] "interneurons_Int16"
## [1] 0
## [1] "Fold enrichment: 1.74998659424822"
## [1] "Standard deviations from mean: 11.0864251611685"
## [1] ""
## [1] "interneurons_Int14"
## [1] 0
## [1] "Fold enrichment: 1.53123958374113"
## [1] "Standard deviations from mean: 7.60370208974289"
## [1] ""
## [1] "interneurons_Int11"
## [1] 0
## [1] "Fold enrichment: 1.5821441869313"
## [1] "Standard deviations from mean: 6.45783327361339"
## [1] ""
## [1] "interneurons_Int5"
## [1] 0
## [1] "Fold enrichment: 2.21581828559141"
## [1] "Standard deviations from mean: 16.1995906005206"
## [1] ""
## [1] "interneurons_Int7"
## [1] 0
## [1] "Fold enrichment: 2.10807282777085"
## [1] "Standard deviations from mean: 15.2154122822341"
## [1] ""
## [1] "interneurons_Int8"
## [1] 0
## [1] "Fold enrichment: 1.62523845934985"
## [1] "Standard deviations from mean: 9.85586312608527"
## [1] ""
## [1] "interneurons_Int12"
## [1] 0
## [1] "Fold enrichment: 1.59812107721362"
## [1] "Standard deviations from mean: 8.34306235492575"
## [1] ""
## [1] "interneurons_Int15"
## [1] 0
## [1] "Fold enrichment: 1.53414431066053"
## [1] "Standard deviations from mean: 7.10081498697409"
## [1] ""
## [1] "pyramidal.SS_.none."
## [1] 1
## [1] ""
## [1] "pyramidal.SS_S1PyrL4"
## [1] 0.9792
## [1] ""
## [1] "pyramidal.SS_ClauPyr"
## [1] 0.9713
## [1] ""
## [1] "pyramidal.SS_S1PyrL5"
## [1] 1
## [1] ""
## [1] "pyramidal.SS_S1PyrL23"
## [1] 1
## [1] ""
## [1] "pyramidal.SS_S1PyrDL"
## [1] 0.9896
## [1] ""
## [1] "pyramidal.SS_S1PyrL5a"
## [1] 0.9944
## [1] ""
## [1] "pyramidal.CA1_SubPyr"
## [1] 0.7803
## [1] ""
## [1] "pyramidal.CA1_CA1Pyr1"
## [1] 1
## [1] ""
## [1] "pyramidal.SS_S1PyrL6b"
## [1] 0.8845
## [1] ""
## [1] "pyramidal.SS_S1PyrL6"
## [1] 0.9976
## [1] ""
## [1] "pyramidal.CA1_CA1Pyr2"
## [1] 1
## [1] ""
## [1] "pyramidal.CA1_CA1PyrInt"
## [1] 0.0057
## [1] "Fold enrichment: 1.17966982801049"
## [1] "Standard deviations from mean: 2.80456658085347"
## [1] ""
## [1] "pyramidal.CA1_CA2Pyr2"
## [1] 1
## [1] ""
## [1] "oligodendrocytes_Oligo1"
## [1] 0
## [1] "Fold enrichment: 1.78923077344274"
## [1] "Standard deviations from mean: 5.92568939282575"
## [1] ""
## [1] "oligodendrocytes_Oligo3"
## [1] 1e-04
## [1] "Fold enrichment: 1.5404012022018"
## [1] "Standard deviations from mean: 4.94273793567662"
## [1] ""
## [1] "oligodendrocytes_Oligo4"
## [1] 0
## [1] "Fold enrichment: 1.7024076579995"
## [1] "Standard deviations from mean: 6.88929124822889"
## [1] ""
## [1] "oligodendrocytes_Oligo2"
## [1] 0
## [1] "Fold enrichment: 1.5783799579134"
## [1] "Standard deviations from mean: 6.24861232193111"
## [1] ""
## [1] "oligodendrocytes_Oligo6"
## [1] 0
## [1] "Fold enrichment: 1.52821395738866"
## [1] "Standard deviations from mean: 6.44720172805403"
## [1] ""
## [1] "oligodendrocytes_Oligo5"
## [1] 0
## [1] "Fold enrichment: 1.49461265330592"
## [1] "Standard deviations from mean: 4.98914296686808"
## [1] ""
## [1] "microglia_Mgl1"
## [1] 0.0095
## [1] "Fold enrichment: 1.52911835533135"
## [1] "Standard deviations from mean: 2.63953062938961"
## [1] ""
## [1] "microglia_Mgl2"
## [1] 0.0405
## [1] "Fold enrichment: 1.38818640475282"
## [1] "Standard deviations from mean: 1.93286556306214"
## [1] ""
## [1] "microglia_Pvm1"
## [1] 0.4283
## [1] ""
## [1] "microglia_Pvm2"
## [1] 0.5602
## [1] ""
## [1] "endothelial.mural_Vsmc"
## [1] 0.4418
## [1] ""
## [1] "endothelial.mural_Vend2"
## [1] 0.471
## [1] ""
## [1] "endothelial.mural_Peric"
## [1] 0.3748
## [1] ""
## [1] "endothelial.mural_.none."
## [1] 0.5677
## [1] ""
## [1] "endothelial.mural_Vend1"
## [1] 0.4315
## [1] ""
## [1] "astrocytes.ependymal_Astro2"
## [1] 0
## [1] "Fold enrichment: 3.31168988565184"
## [1] "Standard deviations from mean: 18.1467356540887"
## [1] ""
## [1] "astrocytes.ependymal_Astro1"
## [1] 0
## [1] "Fold enrichment: 3.59260253988707"
## [1] "Standard deviations from mean: 16.5398102591399"
## [1] ""
## [1] "astrocytes.ependymal_.none."
## [1] 0
## [1] "Fold enrichment: 2.35255686707315"
## [1] "Standard deviations from mean: 11.9206737398829"
## [1] ""
## [1] "astrocytes.ependymal_Choroid"
## [1] 0.034
## [1] "Fold enrichment: 1.380293127569"
## [1] "Standard deviations from mean: 2.06452642472585"
## [1] ""
## [1] "astrocytes.ependymal_Epend"
## [1] 0.1421
## [1] ""
full_results.sub$results[order(full_results.sub$results$p),3:5][1:6,]
## p fold_change sd_from_mean
## interneurons_Int10 0 2.194824 14.013069
## interneurons_Int6 0 1.852575 11.215827
## interneurons_Int9 0 1.958788 9.540774
## interneurons_Int1 0 1.816745 8.710080
## interneurons_Int3 0 2.171368 12.520845
## interneurons_Int13 0 1.810848 9.965006
# Bootstrap plot
ewce.plot(full_results.sub$results,mtc_method="BH")
## 2) Controlling for transcript length and GC content (NOT WORKING)
### Need to convert gene list to human gene symbols
human.hits = unique(m2h[m2h$HGNC.symbol %in% example_genelist,"HGNC.symbol"])
human.bg = unique(setdiff(m2h$HGNC.symbol,human.hits))
# Bootstrap significance testing controlling for transcript length and GC content
#cont_results.sub = bootstrap.enrichment.test(sct_data=celltype_data,
# human.hits=human.hits, human.bg=human.bg,
# reps=10000, sub=T, geneSizeControl=T)
#ewce.plot(cont_results.sub$results,mtc_method="BH")
# 1) Bootstrap significance testing without controlling for transcript length and GC content
full_results = bootstrap.enrichment.test(sct_data=celltype_data,
mouse.hits=mouse.hits,mouse.bg=mouse.bg,
reps=10000, sub=F)
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 2.09136588810587"
## [1] "Standard deviations from mean: 10.0845867070809"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.4713
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.78847125730897"
## [1] "Standard deviations from mean: 16.8515136328113"
## [1] ""
## [1] "microglia"
## [1] 0.0889
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.6166351855409"
## [1] "Standard deviations from mean: 7.25210931801897"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.9995
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
ewce.plot(full_results$results ,mtc_method="BH")
# 1) Controlling for transcript length and GC content (NOT WORKING)
#cont_results = bootstrap.enrichment.test(sct_data=celltype_data,human.hits=human.hits,human.bg=human.bg,reps=reps,sub=F,geneSizeControl=T)
#ewce.plot(cont_results$results,mtc_method="BH")
# Set up gene list
example_genelist <- Mac_mod.tan
library("EWCE")
load(file="~/Desktop/Research/Deconvolution_of_Evolution/celltype_data.rda")
# Find mouse homologues of human genes
data("mouse_to_human_homologs")
m2h = unique(mouse_to_human_homologs[,c("HGNC.symbol","MGI.symbol")])
mouse.hits = unique(m2h[m2h$HGNC.symbol %in% example_genelist,"MGI.symbol"])
mouse.bg = unique(setdiff(m2h$MGI.symbol,mouse.hits))
paste(length(mouse.hits),"/",length(example_genelist),"candidate genes found in sc-RNA-seq reference database.")
## [1] "214 / 226 candidate genes found in sc-RNA-seq reference database."
# Bootstrap significance testing:
## 1) Without controlling for transcript length and GC content
full_results.sub = bootstrap.enrichment.test(sct_data=celltype_data,
mouse.hits=mouse.hits,mouse.bg=mouse.bg,
reps=10000, sub=T)
## [1] "interneurons_Int10"
## [1] 0.0022
## [1] "Fold enrichment: 1.37542231348334"
## [1] "Standard deviations from mean: 3.52907489412147"
## [1] ""
## [1] "interneurons_Int6"
## [1] 0.0388
## [1] "Fold enrichment: 1.19093877316789"
## [1] "Standard deviations from mean: 1.95952748583597"
## [1] ""
## [1] "interneurons_Int9"
## [1] 0.0954
## [1] ""
## [1] "interneurons_Int2"
## [1] 0.9201
## [1] ""
## [1] "interneurons_Int4"
## [1] 0.0752
## [1] ""
## [1] "interneurons_Int1"
## [1] 0.456
## [1] ""
## [1] "interneurons_Int3"
## [1] 0.6313
## [1] ""
## [1] "interneurons_Int13"
## [1] 0.4101
## [1] ""
## [1] "interneurons_Int16"
## [1] 0.0361
## [1] "Fold enrichment: 1.15770545822542"
## [1] "Standard deviations from mean: 1.84888589055422"
## [1] ""
## [1] "interneurons_Int14"
## [1] 0.0371
## [1] "Fold enrichment: 1.1677106808156"
## [1] "Standard deviations from mean: 1.91397854372934"
## [1] ""
## [1] "interneurons_Int11"
## [1] 0.0149
## [1] "Fold enrichment: 1.29210555920499"
## [1] "Standard deviations from mean: 2.54605410331782"
## [1] ""
## [1] "interneurons_Int5"
## [1] 0.0955
## [1] ""
## [1] "interneurons_Int7"
## [1] 0
## [1] "Fold enrichment: 1.46479711630888"
## [1] "Standard deviations from mean: 5.05665145687398"
## [1] ""
## [1] "interneurons_Int8"
## [1] 0.0198
## [1] "Fold enrichment: 1.16908842841573"
## [1] "Standard deviations from mean: 2.13828346706708"
## [1] ""
## [1] "interneurons_Int12"
## [1] 0.0115
## [1] "Fold enrichment: 1.22101604795294"
## [1] "Standard deviations from mean: 2.48330083181076"
## [1] ""
## [1] "interneurons_Int15"
## [1] 0.0892
## [1] ""
## [1] "pyramidal.SS_.none."
## [1] 0.0447
## [1] "Fold enrichment: 1.11291761699782"
## [1] "Standard deviations from mean: 1.71485786508396"
## [1] ""
## [1] "pyramidal.SS_S1PyrL4"
## [1] 0.1121
## [1] ""
## [1] "pyramidal.SS_ClauPyr"
## [1] 0.0435
## [1] "Fold enrichment: 1.25962438397466"
## [1] "Standard deviations from mean: 1.84169436784738"
## [1] ""
## [1] "pyramidal.SS_S1PyrL5"
## [1] 0.0802
## [1] ""
## [1] "pyramidal.SS_S1PyrL23"
## [1] 0.3145
## [1] ""
## [1] "pyramidal.SS_S1PyrDL"
## [1] 0.0532
## [1] ""
## [1] "pyramidal.SS_S1PyrL5a"
## [1] 0.1013
## [1] ""
## [1] "pyramidal.CA1_SubPyr"
## [1] 0.0757
## [1] ""
## [1] "pyramidal.CA1_CA1Pyr1"
## [1] 0.0205
## [1] "Fold enrichment: 1.16088323218451"
## [1] "Standard deviations from mean: 2.13445904485584"
## [1] ""
## [1] "pyramidal.SS_S1PyrL6b"
## [1] 0.2159
## [1] ""
## [1] "pyramidal.SS_S1PyrL6"
## [1] 0.0045
## [1] "Fold enrichment: 1.27575337447446"
## [1] "Standard deviations from mean: 3.26656900134757"
## [1] ""
## [1] "pyramidal.CA1_CA1Pyr2"
## [1] 0.2676
## [1] ""
## [1] "pyramidal.CA1_CA1PyrInt"
## [1] 0.0022
## [1] "Fold enrichment: 1.26042242174832"
## [1] "Standard deviations from mean: 3.24988576786636"
## [1] ""
## [1] "pyramidal.CA1_CA2Pyr2"
## [1] 0.274
## [1] ""
## [1] "oligodendrocytes_Oligo1"
## [1] 1e-04
## [1] "Fold enrichment: 1.86496184531441"
## [1] "Standard deviations from mean: 5.25564195094678"
## [1] ""
## [1] "oligodendrocytes_Oligo3"
## [1] 0
## [1] "Fold enrichment: 1.87659487116985"
## [1] "Standard deviations from mean: 6.41764405238795"
## [1] ""
## [1] "oligodendrocytes_Oligo4"
## [1] 0
## [1] "Fold enrichment: 1.70396316071085"
## [1] "Standard deviations from mean: 5.42162517490978"
## [1] ""
## [1] "oligodendrocytes_Oligo2"
## [1] 0
## [1] "Fold enrichment: 1.85850423772716"
## [1] "Standard deviations from mean: 7.33243443335636"
## [1] ""
## [1] "oligodendrocytes_Oligo6"
## [1] 0
## [1] "Fold enrichment: 1.59232254861078"
## [1] "Standard deviations from mean: 5.71570894832624"
## [1] ""
## [1] "oligodendrocytes_Oligo5"
## [1] 5e-04
## [1] "Fold enrichment: 1.53884964555229"
## [1] "Standard deviations from mean: 4.22554544618381"
## [1] ""
## [1] "microglia_Mgl1"
## [1] 0.0138
## [1] "Fold enrichment: 1.67487138682249"
## [1] "Standard deviations from mean: 2.63425387999052"
## [1] ""
## [1] "microglia_Mgl2"
## [1] 0.08
## [1] ""
## [1] "microglia_Pvm1"
## [1] 0.4666
## [1] ""
## [1] "microglia_Pvm2"
## [1] 0.223
## [1] ""
## [1] "endothelial.mural_Vsmc"
## [1] 0.1736
## [1] ""
## [1] "endothelial.mural_Vend2"
## [1] 0.0228
## [1] "Fold enrichment: 1.40967116962043"
## [1] "Standard deviations from mean: 2.21956100232789"
## [1] ""
## [1] "endothelial.mural_Peric"
## [1] 0.0174
## [1] "Fold enrichment: 1.50660371475035"
## [1] "Standard deviations from mean: 2.46302995459228"
## [1] ""
## [1] "endothelial.mural_.none."
## [1] 0.0647
## [1] ""
## [1] "endothelial.mural_Vend1"
## [1] 0.0023
## [1] "Fold enrichment: 1.76123299275408"
## [1] "Standard deviations from mean: 3.54840899717605"
## [1] ""
## [1] "astrocytes.ependymal_Astro2"
## [1] 0
## [1] "Fold enrichment: 1.98488688740207"
## [1] "Standard deviations from mean: 6.21280188504832"
## [1] ""
## [1] "astrocytes.ependymal_Astro1"
## [1] 0.0016
## [1] "Fold enrichment: 1.79600659835762"
## [1] "Standard deviations from mean: 3.98774558875209"
## [1] ""
## [1] "astrocytes.ependymal_.none."
## [1] 0
## [1] "Fold enrichment: 1.92448169917057"
## [1] "Standard deviations from mean: 6.5041993182344"
## [1] ""
## [1] "astrocytes.ependymal_Choroid"
## [1] 0.0139
## [1] "Fold enrichment: 1.60908586487711"
## [1] "Standard deviations from mean: 2.63564990314784"
## [1] ""
## [1] "astrocytes.ependymal_Epend"
## [1] 0.106
## [1] ""
full_results.sub$results[order(full_results.sub$results$p),3:5][1:6,]
## p fold_change sd_from_mean
## interneurons_Int7 0 1.464797 5.056651
## oligodendrocytes_Oligo3 0 1.876595 6.417644
## oligodendrocytes_Oligo4 0 1.703963 5.421625
## oligodendrocytes_Oligo2 0 1.858504 7.332434
## oligodendrocytes_Oligo6 0 1.592323 5.715709
## astrocytes.ependymal_Astro2 0 1.984887 6.212802
# Bootstrap plot
ewce.plot(full_results.sub$results,mtc_method="BH")
## 2) Controlling for transcript length and GC content (NOT WORKING)
### Need to convert gene list to human gene symbols
human.hits = unique(m2h[m2h$HGNC.symbol %in% example_genelist,"HGNC.symbol"])
human.bg = unique(setdiff(m2h$HGNC.symbol,human.hits))
# Bootstrap significance testing controlling for transcript length and GC content
#cont_results.sub = bootstrap.enrichment.test(sct_data=celltype_data,
# human.hits=human.hits, human.bg=human.bg,
# reps=10000, sub=T, geneSizeControl=T)
#ewce.plot(cont_results.sub$results,mtc_method="BH")
# 1) Bootstrap significance testing without controlling for transcript length and GC content
full_results = bootstrap.enrichment.test(sct_data=celltype_data,
mouse.hits=mouse.hits,mouse.bg=mouse.bg,
reps=10000, sub=F)
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.6344116649678"
## [1] "Standard deviations from mean: 4.60910793868818"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.0018
## [1] "Fold enrichment: 1.4747216946214"
## [1] "Standard deviations from mean: 3.24823103769571"
## [1] ""
## [1] "interneurons"
## [1] 0.0066
## [1] "Fold enrichment: 1.14732797782528"
## [1] "Standard deviations from mean: 2.48761425945757"
## [1] ""
## [1] "microglia"
## [1] 0.0737
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.75891797699607"
## [1] "Standard deviations from mean: 7.06881439891116"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.0374
## [1] "Fold enrichment: 1.1169673151793"
## [1] "Standard deviations from mean: 1.80018737393311"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.0142
## [1] "Fold enrichment: 1.1382324582905"
## [1] "Standard deviations from mean: 2.24844729720863"
## [1] ""
ewce.plot(full_results$results ,mtc_method="BH")
# 1) Controlling for transcript length and GC content (NOT WORKING)
#cont_results = bootstrap.enrichment.test(sct_data=celltype_data,human.hits=human.hits,human.bg=human.bg,reps=reps,sub=F,geneSizeControl=T)
#ewce.plot(cont_results$results,mtc_method="BH")
samples <-ex_mac[,pData(ex_mac)$age=="48 mo"]
region_list <- as.character(unique(pData(samples)$structure_acronym))
DGE_results <- NULL
DGE_summary <- data.frame()
for(region in region_list){
other.samples <-exprs( samples[,pData(samples)$structure_acronym!=region] )
region.samples <-exprs( samples[,pData(samples)$structure_acronym==region] )
# Run DGE
limma.data <-cbind(region.samples, other.samples)
fit <-lmFit(limma.data, design=
c(rep(1,ncol(region.samples)), rep(-1,ncol(other.samples))) )
fit <- eBayes(fit)
tab <- topTable(fit, number=Inf)
tab <- tab[order(-abs(tab$logFC)),]
DGE_results[[paste(region,"vs. all other regions")]] <- list(tab)
# Create summary table
sig <-dim(subset(tab, adj.P.Val<=0.05))[1]
total <- dim(tab)[1]
percent_DEGs <-round(sig / total *100,3)
DGE_summary <- rbind(DGE_summary,data.frame(Test=paste(region,"vs. all other regions"),Sig_DEGs=sig, Total_genes=total, Percent_DEGs=round(sig/total*100,3),
Total_logFC=sum(abs(subset(tab, adj.P.Val<=0.05)$logFC)) )
)
}
# Print written summary
per_DEG <-length(subset(DGE_summary, percent_DEGs==100)$Sig_DEGs)
total_regions <-length(DGE_summary$Test)
paste(per_DEG,"/",total_regions,"adult brain regions have ALL genes differentially expressed.")
## [1] "61 / 61 adult brain regions have ALL genes differentially expressed."
# Results table
pander::pander(subset(DGE_summary[order(c(DGE_summary$Percent_DEGs, -DGE_summary$Total_logFC)),], Test!="<NA>"),style='simple', justify='left', split.table=Inf)
| Test | Sig_DEGs | Total_genes | Percent_DEGs | Total_logFC |
|---|---|---|---|---|
| rCG2 vs. all other regions | 17555 | 17555 | 100 | 128972 |
| rCG3 vs. all other regions | 17555 | 17555 | 100 | 128975 |
| rCG5 vs. all other regions | 17555 | 17555 | 100 | 128966 |
| rCG6 vs. all other regions | 17555 | 17555 | 100 | 128954 |
| dlPF2 vs. all other regions | 17555 | 17555 | 100 | 128969 |
| dlPF3 vs. all other regions | 17555 | 17555 | 100 | 128971 |
| dlPF4 vs. all other regions | 17555 | 17555 | 100 | 128970 |
| dlPF5 vs. all other regions | 17555 | 17555 | 100 | 128970 |
| mOF2 vs. all other regions | 17555 | 17555 | 100 | 129003 |
| mOF3 vs. all other regions | 17555 | 17555 | 100 | 128990 |
| mOF4 vs. all other regions | 17555 | 17555 | 100 | 128985 |
| mOF5 vs. all other regions | 17555 | 17555 | 100 | 128994 |
| mOF6 vs. all other regions | 17555 | 17555 | 100 | 128982 |
| cOF2 vs. all other regions | 17555 | 17555 | 100 | 128977 |
| cOF3 vs. all other regions | 17555 | 17555 | 100 | 128978 |
| cOF5 vs. all other regions | 17555 | 17555 | 100 | 128973 |
| cOF6 vs. all other regions | 17555 | 17555 | 100 | 128982 |
| dlPF6 vs. all other regions | 17555 | 17555 | 100 | 128971 |
| Ca vs. all other regions | 17555 | 17555 | 100 | 130460 |
| ic vs. all other regions | 17555 | 17555 | 100 | 129026 |
| IsCj vs. all other regions | 17555 | 17555 | 100 | 129014 |
| Pu vs. all other regions | 17555 | 17555 | 100 | 129009 |
| Tu vs. all other regions | 17555 | 17555 | 100 | 129009 |
| GPe vs. all other regions | 17555 | 17555 | 100 | 129012 |
| GPi vs. all other regions | 17555 | 17555 | 100 | 129024 |
| V1-1 vs. all other regions | 17555 | 17555 | 100 | 128987 |
| V1-2 vs. all other regions | 17555 | 17555 | 100 | 128998 |
| V1-3 vs. all other regions | 17555 | 17555 | 100 | 128993 |
| V1-4A vs. all other regions | 17555 | 17555 | 100 | 128994 |
| V1-4B vs. all other regions | 17555 | 17555 | 100 | 128996 |
| V14Ca vs. all other regions | 17555 | 17555 | 100 | 129001 |
| V14Cb vs. all other regions | 17555 | 17555 | 100 | 129005 |
| V1-5 vs. all other regions | 17555 | 17555 | 100 | 129001 |
| V1-6 vs. all other regions | 17555 | 17555 | 100 | 129010 |
| V1wm vs. all other regions | 17555 | 17555 | 100 | 129026 |
| V2-2 vs. all other regions | 17555 | 17555 | 100 | 128996 |
| V2-3 vs. all other regions | 17555 | 17555 | 100 | 128991 |
| V2-4 vs. all other regions | 17555 | 17555 | 100 | 128996 |
| V2_L5 vs. all other regions | 17555 | 17555 | 100 | 128994 |
| NAC vs. all other regions | 17555 | 17555 | 100 | 130458 |
| V2-6 vs. all other regions | 17555 | 17555 | 100 | 128996 |
| CA2py vs. all other regions | 17555 | 17555 | 100 | 128813 |
| DGpf vs. all other regions | 17555 | 17555 | 100 | 128794 |
| DGgr vs. all other regions | 17555 | 17555 | 100 | 128823 |
| S vs. all other regions | 17555 | 17555 | 100 | 128795 |
| CA1py vs. all other regions | 17555 | 17555 | 100 | 128807 |
| CA1ra vs. all other regions | 17555 | 17555 | 100 | 128809 |
| CA1or vs. all other regions | 17555 | 17555 | 100 | 130332 |
| DGsg vs. all other regions | 17555 | 17555 | 100 | 128807 |
| CA3py vs. all other regions | 17555 | 17555 | 100 | 128804 |
| AA vs. all other regions | 17555 | 17555 | 100 | 128696 |
| PL vs. all other regions | 17555 | 17555 | 100 | 128700 |
| Me vs. all other regions | 17555 | 17555 | 100 | 128681 |
| L vs. all other regions | 17555 | 17555 | 100 | 128699 |
| APir vs. all other regions | 17555 | 17555 | 100 | 128696 |
| rPAC vs. all other regions | 17555 | 17555 | 100 | 128696 |
| AB vs. all other regions | 17555 | 17555 | 100 | 128699 |
| AHA vs. all other regions | 17555 | 17555 | 100 | 128703 |
| B vs. all other regions | 17555 | 17555 | 100 | 128700 |
| CE vs. all other regions | 17555 | 17555 | 100 | 128703 |
| CA4 vs. all other regions | 17555 | 17555 | 100 | 130333 |
## [1] "astrocytes-ependymal"
## [1] 0.69
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.92
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.74
## [1] ""
## [1] "endothelial-mural"
## [1] 0.49
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.36149578773616"
## [1] "Standard deviations from mean: 7.73043290085362"
## [1] ""
## [1] "microglia"
## [1] 0.54
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.48706613495683"
## [1] "Standard deviations from mean: 5.39412006371251"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.3006910218487"
## [1] "Standard deviations from mean: 5.41294367034982"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30306114510364"
## [1] "Standard deviations from mean: 5.52351082686908"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.82
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.73
## [1] ""
## [1] "endothelial-mural"
## [1] 0.35
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.3700680438909"
## [1] "Standard deviations from mean: 8.82309287938557"
## [1] ""
## [1] "microglia"
## [1] 0.2
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.33279184192791"
## [1] "Standard deviations from mean: 3.41109767616896"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33815230296478"
## [1] "Standard deviations from mean: 5.98798414409446"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.27629280460224"
## [1] "Standard deviations from mean: 5.37088813639783"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.49
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.88
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.28
## [1] ""
## [1] "endothelial-mural"
## [1] 0.19
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.29227703613989"
## [1] "Standard deviations from mean: 6.19301474960127"
## [1] ""
## [1] "microglia"
## [1] 0.16
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.32885384541189"
## [1] "Standard deviations from mean: 3.80231261579436"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.29004148629611"
## [1] "Standard deviations from mean: 5.18060664161643"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.24766903001379"
## [1] "Standard deviations from mean: 4.60126470944077"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.04
## [1] "Fold enrichment: 1.20971624101054"
## [1] "Standard deviations from mean: 1.81828033235132"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.16
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.27
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.78
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.96
## [1] ""
## [1] "endothelial-mural"
## [1] 0.86
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.50224653938415"
## [1] "Standard deviations from mean: 10.3053919303254"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.33
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.35948375100978"
## [1] "Standard deviations from mean: 6.40297990697599"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.4582057192322"
## [1] "Standard deviations from mean: 8.9655423773847"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.57
## [1] ""
## [1] "endothelial-mural"
## [1] 0.93
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.94
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.81
## [1] ""
## [1] "endothelial-mural"
## [1] 0.78
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31816169598577"
## [1] "Standard deviations from mean: 6.33899284234282"
## [1] ""
## [1] "microglia"
## [1] 0.23
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.37552893584322"
## [1] "Standard deviations from mean: 4.52055576250819"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.18244959149805"
## [1] "Standard deviations from mean: 3.08959177183019"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.25913868902883"
## [1] "Standard deviations from mean: 5.01154847532714"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.87
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.86
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.64
## [1] ""
## [1] "endothelial-mural"
## [1] 0.23
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.36050804340364"
## [1] "Standard deviations from mean: 7.24795800993131"
## [1] ""
## [1] "microglia"
## [1] 0.18
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.35692691069638"
## [1] "Standard deviations from mean: 4.40146608043808"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.28986462214001"
## [1] "Standard deviations from mean: 5.52065620741554"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.24423875640361"
## [1] "Standard deviations from mean: 4.54606290633971"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.83
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.91
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.65
## [1] ""
## [1] "endothelial-mural"
## [1] 0.76
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31295017479235"
## [1] "Standard deviations from mean: 6.17871887421864"
## [1] ""
## [1] "microglia"
## [1] 0.62
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.32522978286158"
## [1] "Standard deviations from mean: 3.49998495408187"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33746135195364"
## [1] "Standard deviations from mean: 6.49826352264873"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.29597272196383"
## [1] "Standard deviations from mean: 6.08073710174888"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.67
## [1] ""
## [1] "endothelial-mural"
## [1] 0.99
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.89
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.07
## [1] ""
## [1] "endothelial-mural"
## [1] 0.14
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.22651466651052"
## [1] "Standard deviations from mean: 4.01167772102314"
## [1] ""
## [1] "microglia"
## [1] 0.26
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.43042105463237"
## [1] "Standard deviations from mean: 4.35821391837049"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.24786678263757"
## [1] "Standard deviations from mean: 4.37249239022044"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.23438255030681"
## [1] "Standard deviations from mean: 4.56287400363116"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.75
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.91
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.97
## [1] ""
## [1] "endothelial-mural"
## [1] 0.91
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.43380662021178"
## [1] "Standard deviations from mean: 7.73867774850157"
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.3834688946679"
## [1] "Standard deviations from mean: 3.8713145386966"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.35458476242393"
## [1] "Standard deviations from mean: 6.50205763876491"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.3680353776727"
## [1] "Standard deviations from mean: 6.14124278687038"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.83
## [1] ""
## [1] "endothelial-mural"
## [1] 0.99
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.9
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.89
## [1] ""
## [1] "endothelial-mural"
## [1] 0.73
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.44843776981431"
## [1] "Standard deviations from mean: 8.96532085343343"
## [1] ""
## [1] "microglia"
## [1] 0.59
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.37377953042244"
## [1] "Standard deviations from mean: 4.57305150798601"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.31812101054864"
## [1] "Standard deviations from mean: 5.22532442557582"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.33090714349838"
## [1] "Standard deviations from mean: 5.80603267628215"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.84
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.95
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.84
## [1] ""
## [1] "endothelial-mural"
## [1] 0.97
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.34759803172799"
## [1] "Standard deviations from mean: 6.79346066332768"
## [1] ""
## [1] "microglia"
## [1] 0.51
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.25766584745416"
## [1] "Standard deviations from mean: 2.56206833777982"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.36339185402269"
## [1] "Standard deviations from mean: 7.11731453956082"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.3373779339942"
## [1] "Standard deviations from mean: 6.45608084307466"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.82
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.95
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.82
## [1] ""
## [1] "endothelial-mural"
## [1] 0.73
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.38137625614076"
## [1] "Standard deviations from mean: 7.51625656165242"
## [1] ""
## [1] "microglia"
## [1] 0.66
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.03
## [1] "Fold enrichment: 1.25493492942026"
## [1] "Standard deviations from mean: 2.40834151792195"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.39930295518793"
## [1] "Standard deviations from mean: 6.81153007756045"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.39106929297621"
## [1] "Standard deviations from mean: 7.78880036008649"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.11
## [1] ""
## [1] "endothelial-mural"
## [1] 0.43
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.66
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.69
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.99
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.5621222004961"
## [1] "Standard deviations from mean: 12.5150093541627"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.43
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.40467918113106"
## [1] "Standard deviations from mean: 6.80783994245793"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.45913555622893"
## [1] "Standard deviations from mean: 8.29765048082985"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.66
## [1] ""
## [1] "endothelial-mural"
## [1] 0.99
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.87
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.74
## [1] ""
## [1] "endothelial-mural"
## [1] 0.79
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.44795242248311"
## [1] "Standard deviations from mean: 8.45665807903486"
## [1] ""
## [1] "microglia"
## [1] 0.71
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.51039283321374"
## [1] "Standard deviations from mean: 5.93001116533696"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.32773122803151"
## [1] "Standard deviations from mean: 5.67429368859355"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.36909390523736"
## [1] "Standard deviations from mean: 5.91227369102989"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.78
## [1] ""
## [1] "endothelial-mural"
## [1] 0.95
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.79
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.61
## [1] ""
## [1] "endothelial-mural"
## [1] 0.36
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.39486214796231"
## [1] "Standard deviations from mean: 7.37205193558631"
## [1] ""
## [1] "microglia"
## [1] 0.12
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.24482744229959"
## [1] "Standard deviations from mean: 2.53644643417514"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.28943834448305"
## [1] "Standard deviations from mean: 6.23199336799741"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.21958585957237"
## [1] "Standard deviations from mean: 4.64186024386257"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.75
## [1] ""
## [1] "endothelial-mural"
## [1] 0.98
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.92
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.36
## [1] ""
## [1] "endothelial-mural"
## [1] 0.13
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30844483976371"
## [1] "Standard deviations from mean: 6.24877224873451"
## [1] ""
## [1] "microglia"
## [1] 0.57
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.34055306650585"
## [1] "Standard deviations from mean: 3.75054657923674"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33344011129399"
## [1] "Standard deviations from mean: 5.6710161103249"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.2992959543193"
## [1] "Standard deviations from mean: 5.77464691151524"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.31
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.85
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.59
## [1] ""
## [1] "endothelial-mural"
## [1] 0.59
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.45991365654631"
## [1] "Standard deviations from mean: 8.33501477494695"
## [1] ""
## [1] "microglia"
## [1] 0.76
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.25
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33185773870414"
## [1] "Standard deviations from mean: 5.27417113038539"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.35989210449217"
## [1] "Standard deviations from mean: 6.03018789238473"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.15
## [1] ""
## [1] "endothelial-mural"
## [1] 0.79
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.62
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.79
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.84
## [1] ""
## [1] "endothelial-mural"
## [1] 0.93
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.49085975083854"
## [1] "Standard deviations from mean: 10.2236170140348"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.57
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.32092329330656"
## [1] "Standard deviations from mean: 5.88231463180095"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.3579236911614"
## [1] "Standard deviations from mean: 6.5865384050543"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.13
## [1] ""
## [1] "endothelial-mural"
## [1] 0.15
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.54
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.47
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.98
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.95
## [1] ""
## [1] "endothelial-mural"
## [1] 0.94
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.38997916859193"
## [1] "Standard deviations from mean: 8.11429841638261"
## [1] ""
## [1] "microglia"
## [1] 0.51
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.17332537916424"
## [1] "Standard deviations from mean: 1.90264302432939"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.32356707887264"
## [1] "Standard deviations from mean: 6.09647703284844"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.31144411926353"
## [1] "Standard deviations from mean: 5.94006264918281"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.06
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.38551373175015"
## [1] "Standard deviations from mean: 2.92079510642893"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.01
## [1] "Fold enrichment: 1.46018622778369"
## [1] "Standard deviations from mean: 2.57631933103226"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 2.16112033661971"
## [1] "Standard deviations from mean: 13.3692834699465"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.57188130086226"
## [1] "Standard deviations from mean: 11.3201395537844"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.55138764316653"
## [1] "Standard deviations from mean: 10.1874641509226"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.67894614152506"
## [1] "Standard deviations from mean: 13.4887628005985"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.4031458891024"
## [1] "Standard deviations from mean: 3.13677762894169"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.13
## [1] ""
## [1] "interneurons"
## [1] 0.93
## [1] ""
## [1] "microglia"
## [1] 0.72
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.89
## [1] ""
## [1] "endothelial-mural"
## [1] 0.44
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.27628832509747"
## [1] "Standard deviations from mean: 5.2235438343547"
## [1] ""
## [1] "microglia"
## [1] 0.54
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.29
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.38776064644479"
## [1] "Standard deviations from mean: 6.3662912677179"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.55067257381571"
## [1] "Standard deviations from mean: 9.51482252806637"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.06
## [1] ""
## [1] "endothelial-mural"
## [1] 0.09
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.47
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.12
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.76
## [1] ""
## [1] "endothelial-mural"
## [1] 0.97
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.37379638148696"
## [1] "Standard deviations from mean: 6.2052097514887"
## [1] ""
## [1] "microglia"
## [1] 0.84
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.28
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.23137724758419"
## [1] "Standard deviations from mean: 3.76432407243025"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.41765917241121"
## [1] "Standard deviations from mean: 7.30602505474287"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.12
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.32
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.94
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.97
## [1] ""
## [1] "pyramidal SS"
## [1] 0.99
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.85
## [1] ""
## [1] "endothelial-mural"
## [1] 0.78
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.33845119170987"
## [1] "Standard deviations from mean: 6.47556238057059"
## [1] ""
## [1] "microglia"
## [1] 0.84
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.08
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.30421348835203"
## [1] "Standard deviations from mean: 5.3959306894098"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.43080122001797"
## [1] "Standard deviations from mean: 7.41109266741336"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.31319351819271"
## [1] "Standard deviations from mean: 2.48344244904012"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.01
## [1] "Fold enrichment: 1.38462282582869"
## [1] "Standard deviations from mean: 3.06937544847777"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.01
## [1] "Fold enrichment: 1.43441833751683"
## [1] "Standard deviations from mean: 2.58739155741733"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.89821029534966"
## [1] "Standard deviations from mean: 10.034880972535"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.91
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.24251646983205"
## [1] "Standard deviations from mean: 4.54464068539368"
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.76753524229695"
## [1] "Standard deviations from mean: 13.1607758421138"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.79677233574885"
## [1] "Standard deviations from mean: 12.9741861694883"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.3639194869393"
## [1] "Standard deviations from mean: 3.01911792253967"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.49382403494025"
## [1] "Standard deviations from mean: 3.62526766796914"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0
## [1] "Fold enrichment: 1.58106408175289"
## [1] "Standard deviations from mean: 3.35463193938967"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.85819896588427"
## [1] "Standard deviations from mean: 7.83543414462605"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31058239027344"
## [1] "Standard deviations from mean: 6.24780704095568"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.83070120165711"
## [1] "Standard deviations from mean: 14.6665482503819"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.68803526349134"
## [1] "Standard deviations from mean: 13.4787845822556"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.52609664045784"
## [1] "Standard deviations from mean: 4.19083836259959"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.29726131132156"
## [1] "Standard deviations from mean: 2.59075735818402"
## [1] ""
## [1] "interneurons"
## [1] 0.97
## [1] ""
## [1] "microglia"
## [1] 0.26
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.87
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.53005309432026"
## [1] "Standard deviations from mean: 10.8629930557857"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.38
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.47859459775011"
## [1] "Standard deviations from mean: 8.52171671270359"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.46840477664178"
## [1] "Standard deviations from mean: 9.45872288692324"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.24
## [1] ""
## [1] "endothelial-mural"
## [1] 0.6
## [1] ""
## [1] "interneurons"
## [1] 0.75
## [1] ""
## [1] "microglia"
## [1] 0.93
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.97
## [1] ""
## [1] "pyramidal SS"
## [1] 0.96
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.27875253978408"
## [1] "Standard deviations from mean: 2.21498627935773"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.19
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.16510157978425"
## [1] "Standard deviations from mean: 3.0757711352291"
## [1] ""
## [1] "microglia"
## [1] 0.06
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.61898283171582"
## [1] "Standard deviations from mean: 6.42824918717225"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.02
## [1] "Fold enrichment: 1.14738623521404"
## [1] "Standard deviations from mean: 2.43351059970434"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.06
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.06
## [1] ""
## [1] "endothelial-mural"
## [1] 0.71
## [1] ""
## [1] "interneurons"
## [1] 0.52
## [1] ""
## [1] "microglia"
## [1] 0.96
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.98
## [1] ""
## [1] "pyramidal SS"
## [1] 0.46
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.02
## [1] "Fold enrichment: 1.27013492529625"
## [1] "Standard deviations from mean: 2.53201931850234"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.36960966205838"
## [1] "Standard deviations from mean: 3.44243983266395"
## [1] ""
## [1] "interneurons"
## [1] 0.02
## [1] "Fold enrichment: 1.10602440785071"
## [1] "Standard deviations from mean: 1.89147014239509"
## [1] ""
## [1] "microglia"
## [1] 0.17
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.48260197129409"
## [1] "Standard deviations from mean: 4.94510810352423"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.15221985187446"
## [1] "Standard deviations from mean: 2.64064710896877"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.06
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.05
## [1] ""
## [1] "endothelial-mural"
## [1] 0.77
## [1] ""
## [1] "interneurons"
## [1] 0.95
## [1] ""
## [1] "microglia"
## [1] 0.81
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.95
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.99
## [1] ""
## [1] "pyramidal SS"
## [1] 0.6
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.16
## [1] ""
## [1] "endothelial-mural"
## [1] 0.52
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.33704865099281"
## [1] "Standard deviations from mean: 7.40613869834812"
## [1] ""
## [1] "microglia"
## [1] 0.64
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.33537098255361"
## [1] "Standard deviations from mean: 3.31012553162413"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.38474998404645"
## [1] "Standard deviations from mean: 6.57224942160014"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.28697578921463"
## [1] "Standard deviations from mean: 5.16826741412223"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.03
## [1] "Fold enrichment: 1.20956718731157"
## [1] "Standard deviations from mean: 2.01098547163833"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.29
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.68
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.15
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.79
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.38
## [1] ""
## [1] "endothelial-mural"
## [1] 0.78
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.38636567606745"
## [1] "Standard deviations from mean: 7.27021212077539"
## [1] ""
## [1] "microglia"
## [1] 0.89
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.04
## [1] "Fold enrichment: 1.17538494431609"
## [1] "Standard deviations from mean: 1.84240286560029"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.42473904666186"
## [1] "Standard deviations from mean: 7.55860337421488"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30049635743458"
## [1] "Standard deviations from mean: 5.45693806525425"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.08
## [1] ""
## [1] "endothelial-mural"
## [1] 0.39
## [1] ""
## [1] "interneurons"
## [1] 0.97
## [1] ""
## [1] "microglia"
## [1] 0.81
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.79
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.5
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.4
## [1] ""
## [1] "endothelial-mural"
## [1] 0.8
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.29144465882492"
## [1] "Standard deviations from mean: 6.22949262910354"
## [1] ""
## [1] "microglia"
## [1] 0.61
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.05
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.46970065256005"
## [1] "Standard deviations from mean: 7.94294569981396"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30390038215338"
## [1] "Standard deviations from mean: 6.27551010597199"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.05
## [1] ""
## [1] "endothelial-mural"
## [1] 0.65
## [1] ""
## [1] "interneurons"
## [1] 0.94
## [1] ""
## [1] "microglia"
## [1] 0.66
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.99
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.99
## [1] ""
## [1] "pyramidal SS"
## [1] 0.12
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.54
## [1] ""
## [1] "endothelial-mural"
## [1] 0.96
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31394892835994"
## [1] "Standard deviations from mean: 6.1173938461333"
## [1] ""
## [1] "microglia"
## [1] 0.48
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.13
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.44779456916031"
## [1] "Standard deviations from mean: 8.22710021064428"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.29153853239118"
## [1] "Standard deviations from mean: 5.10817757482449"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.32915673278872"
## [1] "Standard deviations from mean: 2.63273952641479"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.62
## [1] ""
## [1] "interneurons"
## [1] 0.99
## [1] ""
## [1] "microglia"
## [1] 0.93
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.92
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.72
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.07
## [1] ""
## [1] "endothelial-mural"
## [1] 0.11
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30287907402794"
## [1] "Standard deviations from mean: 6.40418355880191"
## [1] ""
## [1] "microglia"
## [1] 0.28
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.37619185813792"
## [1] "Standard deviations from mean: 3.88909548278151"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.34925567557713"
## [1] "Standard deviations from mean: 6.16835467328472"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.20523887570248"
## [1] "Standard deviations from mean: 3.73047543861585"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.02
## [1] "Fold enrichment: 1.2345078048121"
## [1] "Standard deviations from mean: 2.00325776182715"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.39
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.79
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.91
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.87
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.42996639958263"
## [1] "Standard deviations from mean: 3.28211202146089"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.01
## [1] "Fold enrichment: 1.2803790310865"
## [1] "Standard deviations from mean: 2.40068779294221"
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.25313038455265"
## [1] "Standard deviations from mean: 5.3863020655235"
## [1] ""
## [1] "microglia"
## [1] 0.18
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.44535137179669"
## [1] "Standard deviations from mean: 4.90458785086483"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.29068397841707"
## [1] "Standard deviations from mean: 4.93727519251717"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.16841017166969"
## [1] "Standard deviations from mean: 3.46700544574065"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.36696360234368"
## [1] "Standard deviations from mean: 2.93024584295815"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.41794909698978"
## [1] "Standard deviations from mean: 3.17077121039667"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0
## [1] "Fold enrichment: 1.594983480188"
## [1] "Standard deviations from mean: 3.86912857865004"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 2.09605843990363"
## [1] "Standard deviations from mean: 11.9798183878005"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.55698264119325"
## [1] "Standard deviations from mean: 10.6010692407944"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.67180991235752"
## [1] "Standard deviations from mean: 11.3869500487921"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.66022158463122"
## [1] "Standard deviations from mean: 11.5082549610787"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.15
## [1] ""
## [1] "endothelial-mural"
## [1] 0.57
## [1] ""
## [1] "interneurons"
## [1] 0.93
## [1] ""
## [1] "microglia"
## [1] 0.95
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.96
## [1] ""
## [1] "pyramidal SS"
## [1] 0.91
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.19
## [1] ""
## [1] "endothelial-mural"
## [1] 0.01
## [1] "Fold enrichment: 1.40057184391778"
## [1] "Standard deviations from mean: 2.55313146551903"
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0
## [1] "Fold enrichment: 1.39996186724745"
## [1] "Standard deviations from mean: 2.31039974252778"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.06
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.04
## [1] "Fold enrichment: 1.21362653994441"
## [1] "Standard deviations from mean: 1.73082803952873"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.47
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.8
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.97
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.89
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.09
## [1] ""
## [1] "endothelial-mural"
## [1] 0.28
## [1] ""
## [1] "interneurons"
## [1] 0.97
## [1] ""
## [1] "microglia"
## [1] 0.24
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.09
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.46
## [1] ""
## [1] "pyramidal SS"
## [1] 0.91
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.07
## [1] ""
## [1] "endothelial-mural"
## [1] 0.75
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.9
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.48
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.62
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.08
## [1] ""
## [1] "endothelial-mural"
## [1] 0.45
## [1] ""
## [1] "interneurons"
## [1] 0.73
## [1] ""
## [1] "microglia"
## [1] 0.05
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.86
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.11946937833408"
## [1] "Standard deviations from mean: 2.34446131585096"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.37
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.14
## [1] ""
## [1] "endothelial-mural"
## [1] 0.19
## [1] ""
## [1] "interneurons"
## [1] 0.99
## [1] ""
## [1] "microglia"
## [1] 0.84
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.4
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.9
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.23
## [1] ""
## [1] "endothelial-mural"
## [1] 0.05
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.08
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.72
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.9
## [1] ""
## [1] "pyramidal SS"
## [1] 0.92
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.05
## [1] ""
## [1] "endothelial-mural"
## [1] 0.17
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.37
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.95
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.94
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.94
## [1] ""
## [1] "endothelial-mural"
## [1] 0.84
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.32337264890325"
## [1] "Standard deviations from mean: 6.2116837511867"
## [1] ""
## [1] "microglia"
## [1] 0.79
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.27508029866987"
## [1] "Standard deviations from mean: 2.8786375666947"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.30343902889344"
## [1] "Standard deviations from mean: 4.93204992614422"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.43055354120573"
## [1] "Standard deviations from mean: 8.31947139445578"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.14
## [1] ""
## [1] "endothelial-mural"
## [1] 0.24
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.76
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.26223151161346"
## [1] "Standard deviations from mean: 2.59043133720525"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.99
## [1] ""
## [1] "pyramidal SS"
## [1] 0.98
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.36870464124298"
## [1] "Standard deviations from mean: 3.34394567074562"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.16
## [1] ""
## [1] "interneurons"
## [1] 0.73
## [1] ""
## [1] "microglia"
## [1] 0.74
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.7
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.78
## [1] ""
## [1] "pyramidal SS"
## [1] 0.97
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.34115719802433"
## [1] "Standard deviations from mean: 2.90876896293013"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.34
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.95
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.99
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.58
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.31
## [1] ""
## [1] "endothelial-mural"
## [1] 0.01
## [1] "Fold enrichment: 1.3627028838159"
## [1] "Standard deviations from mean: 2.70302009986228"
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.31537620067647"
## [1] "Standard deviations from mean: 5.98986609686851"
## [1] ""
## [1] "microglia"
## [1] 0.2
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.33364386151528"
## [1] "Standard deviations from mean: 4.02450240208077"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.01
## [1] "Fold enrichment: 1.13076377124095"
## [1] "Standard deviations from mean: 2.51095789921836"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.28925562490391"
## [1] "Standard deviations from mean: 5.61668594748418"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.4823749819384"
## [1] "Standard deviations from mean: 3.93833960224507"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.65
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.82
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.39908119770173"
## [1] "Standard deviations from mean: 8.20790986568399"
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.32
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.47412414157004"
## [1] "Standard deviations from mean: 7.84349015856196"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.49911975638761"
## [1] "Standard deviations from mean: 9.56659473537242"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.03
## [1] "Fold enrichment: 1.2633411821024"
## [1] "Standard deviations from mean: 2.25059962967349"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.43
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.92
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.67
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 2.34438698015671"
## [1] "Standard deviations from mean: 10.2850431979544"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 2.20934908576778"
## [1] "Standard deviations from mean: 10.4605140577291"
## [1] ""
## [1] "interneurons"
## [1] 0.3
## [1] ""
## [1] "microglia"
## [1] 0.04
## [1] "Fold enrichment: 1.41271134947188"
## [1] "Standard deviations from mean: 2.3107487415948"
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.46441253180761"
## [1] "Standard deviations from mean: 4.68129096454793"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 0.97
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.03
## [1] "Fold enrichment: 1.25848007054243"
## [1] "Standard deviations from mean: 2.02384462845744"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.43
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.86
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.9
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.95
## [1] ""
## [1] "pyramidal SS"
## [1] 0.99
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.9
## [1] ""
## [1] "endothelial-mural"
## [1] 0.43
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30008675377373"
## [1] "Standard deviations from mean: 5.42031403715552"
## [1] ""
## [1] "microglia"
## [1] 0.54
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.06
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.28613948352802"
## [1] "Standard deviations from mean: 4.92855167221698"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.28062017245432"
## [1] "Standard deviations from mean: 5.31129428111827"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.94
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.11447815932378"
## [1] "Standard deviations from mean: 2.09159270771025"
## [1] ""
## [1] "pyramidal SS"
## [1] 0.99
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.53
## [1] ""
## [1] "endothelial-mural"
## [1] 0.1
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.2567000331111"
## [1] "Standard deviations from mean: 5.823761815847"
## [1] ""
## [1] "microglia"
## [1] 0.64
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.04
## [1] "Fold enrichment: 1.14249586110947"
## [1] "Standard deviations from mean: 1.67251676999416"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.65
## [1] ""
## [1] "pyramidal SS"
## [1] 0.06
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.41971612339772"
## [1] "Standard deviations from mean: 3.51058535564215"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.35
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.2
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.89
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.98
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.4150074517071"
## [1] "Standard deviations from mean: 8.5208885544443"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.26
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.41655204777831"
## [1] "Standard deviations from mean: 7.16171857119791"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.4073703625943"
## [1] "Standard deviations from mean: 7.96075859275961"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.2578688324965"
## [1] "Standard deviations from mean: 2.52283635811912"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.33
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.09
## [1] ""
## [1] "oligodendrocytes"
## [1] 0
## [1] "Fold enrichment: 1.34124327189425"
## [1] "Standard deviations from mean: 3.39495369391125"
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.99
## [1] ""
## [1] "endothelial-mural"
## [1] 0.98
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.43538308689614"
## [1] "Standard deviations from mean: 8.39895492264893"
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.68
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.45138433967613"
## [1] "Standard deviations from mean: 7.92033419798832"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.48043339961085"
## [1] "Standard deviations from mean: 9.22131641168356"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.38763056109799"
## [1] "Standard deviations from mean: 3.67978337177896"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.31
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.93
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.45
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.41037174922249"
## [1] "Standard deviations from mean: 3.90731252645131"
## [1] ""
## [1] "endothelial-mural"
## [1] 0
## [1] "Fold enrichment: 1.91497462930138"
## [1] "Standard deviations from mean: 7.03185915983936"
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.28375074944581"
## [1] "Standard deviations from mean: 5.59750081235203"
## [1] ""
## [1] "microglia"
## [1] 0.33
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.09
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.44
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.18402102170687"
## [1] "Standard deviations from mean: 3.18818693941427"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.30998778171148"
## [1] "Standard deviations from mean: 2.65563038799159"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.68
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.94
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.97
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.84
## [1] ""
## [1] "endothelial-mural"
## [1] 0.14
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30643621630414"
## [1] "Standard deviations from mean: 6.28323424726205"
## [1] ""
## [1] "microglia"
## [1] 0.75
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.42
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.08
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.22096190760641"
## [1] "Standard deviations from mean: 4.20758763015106"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.4233602033347"
## [1] "Standard deviations from mean: 3.34655526057046"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.4
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.53
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.98
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.99
## [1] ""
## [1] "endothelial-mural"
## [1] 0.95
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.37142565889519"
## [1] "Standard deviations from mean: 7.61821600874018"
## [1] ""
## [1] "microglia"
## [1] 1
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.13
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.5281425836805"
## [1] "Standard deviations from mean: 9.34630109808363"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.60026886047134"
## [1] "Standard deviations from mean: 11.5171999874655"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.40351045744542"
## [1] "Standard deviations from mean: 3.02864627650639"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.79
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.86
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.56
## [1] ""
## [1] "endothelial-mural"
## [1] 0.94
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.43321983170287"
## [1] "Standard deviations from mean: 8.98127431405562"
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.02
## [1] "Fold enrichment: 1.18443743673693"
## [1] "Standard deviations from mean: 2.09424180798725"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.25079983630804"
## [1] "Standard deviations from mean: 4.1592618720545"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.33007645619501"
## [1] "Standard deviations from mean: 6.20145618524968"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.43902964300419"
## [1] "Standard deviations from mean: 3.51203374583504"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.81
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.8
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.97
## [1] ""
## [1] "endothelial-mural"
## [1] 0.96
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.3527570522622"
## [1] "Standard deviations from mean: 6.4564748117626"
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.21787848660516"
## [1] "Standard deviations from mean: 2.40407228399115"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.50680502603419"
## [1] "Standard deviations from mean: 9.74149985433321"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.60480848128911"
## [1] "Standard deviations from mean: 12.3279086990675"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.30165031481362"
## [1] "Standard deviations from mean: 2.91894882639394"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.27
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.96
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.99
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.91
## [1] ""
## [1] "endothelial-mural"
## [1] 0.74
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.43562743942985"
## [1] "Standard deviations from mean: 8.29866369629902"
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.19
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33514251994228"
## [1] "Standard deviations from mean: 5.80942661119468"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30381838199839"
## [1] "Standard deviations from mean: 5.68950336488475"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.49086306422457"
## [1] "Standard deviations from mean: 4.28604372228488"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.42
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.84
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 0.8
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.88
## [1] ""
## [1] "endothelial-mural"
## [1] 0.95
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.34612727454254"
## [1] "Standard deviations from mean: 6.59714653108469"
## [1] ""
## [1] "microglia"
## [1] 0.94
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.11
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.27915196135261"
## [1] "Standard deviations from mean: 5.05761409564878"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.38152487175855"
## [1] "Standard deviations from mean: 7.27000855974018"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.41975936273514"
## [1] "Standard deviations from mean: 3.98120346291382"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.25
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.92
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.83
## [1] ""
## [1] "endothelial-mural"
## [1] 0.91
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.39864057422312"
## [1] "Standard deviations from mean: 8.61198427654143"
## [1] ""
## [1] "microglia"
## [1] 0.98
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.22448644954557"
## [1] "Standard deviations from mean: 2.44343527043834"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.34649957554668"
## [1] "Standard deviations from mean: 6.85332191081762"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.35156421103183"
## [1] "Standard deviations from mean: 6.30664236799328"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.06
## [1] ""
## [1] "endothelial-mural"
## [1] 0.96
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.92
## [1] ""
## [1] "endothelial-mural"
## [1] 0.76
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.35758518927623"
## [1] "Standard deviations from mean: 7.00350624455461"
## [1] ""
## [1] "microglia"
## [1] 0.99
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.04
## [1] "Fold enrichment: 1.20246503521053"
## [1] "Standard deviations from mean: 1.8767100555157"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.24454338653484"
## [1] "Standard deviations from mean: 4.05436476036237"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.30299518195013"
## [1] "Standard deviations from mean: 5.11873168238985"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.44621033351169"
## [1] "Standard deviations from mean: 3.52925041663019"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.5
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 1
## [1] ""
## [1] "endothelial-mural"
## [1] 0.92
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.30524616238526"
## [1] "Standard deviations from mean: 5.57754542390505"
## [1] ""
## [1] "microglia"
## [1] 0.93
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.01
## [1] "Fold enrichment: 1.19110345839714"
## [1] "Standard deviations from mean: 2.36978916297784"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.33112906211396"
## [1] "Standard deviations from mean: 5.80698816967745"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.43015870269901"
## [1] "Standard deviations from mean: 8.12654218634643"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.37710612615752"
## [1] "Standard deviations from mean: 3.25072805984142"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.44
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.64
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.99
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.87
## [1] ""
## [1] "endothelial-mural"
## [1] 0.66
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.44891705400785"
## [1] "Standard deviations from mean: 8.76072691559011"
## [1] ""
## [1] "microglia"
## [1] 0.94
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.05
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.32038508387217"
## [1] "Standard deviations from mean: 5.83248576316005"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.37374233436999"
## [1] "Standard deviations from mean: 6.34668263502661"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0
## [1] "Fold enrichment: 1.44022400912604"
## [1] "Standard deviations from mean: 3.55072306146624"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.48
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.85
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.8
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.98
## [1] ""
## [1] "endothelial-mural"
## [1] 0.97
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.40709698005332"
## [1] "Standard deviations from mean: 8.09206400945743"
## [1] ""
## [1] "microglia"
## [1] 0.96
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.26
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.47119020717666"
## [1] "Standard deviations from mean: 8.57557975114125"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.58380281696041"
## [1] "Standard deviations from mean: 10.5441974474243"
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.01
## [1] "Fold enrichment: 1.21903708533958"
## [1] "Standard deviations from mean: 1.87730072497057"
## [1] ""
## [1] "endothelial-mural"
## [1] 0.42
## [1] ""
## [1] "interneurons"
## [1] 1
## [1] ""
## [1] "microglia"
## [1] 0.97
## [1] ""
## [1] "oligodendrocytes"
## [1] 1
## [1] ""
## [1] "pyramidal CA1"
## [1] 1
## [1] ""
## [1] "pyramidal SS"
## [1] 1
## [1] ""
## [1] "astrocytes-ependymal"
## [1] 0.67
## [1] ""
## [1] "endothelial-mural"
## [1] 0.31
## [1] ""
## [1] "interneurons"
## [1] 0
## [1] "Fold enrichment: 1.27071434528459"
## [1] "Standard deviations from mean: 5.2285046862428"
## [1] ""
## [1] "microglia"
## [1] 0.45
## [1] ""
## [1] "oligodendrocytes"
## [1] 0.02
## [1] "Fold enrichment: 1.19564719049588"
## [1] "Standard deviations from mean: 2.17454988211899"
## [1] ""
## [1] "pyramidal CA1"
## [1] 0
## [1] "Fold enrichment: 1.14900771945043"
## [1] "Standard deviations from mean: 2.71585040616355"
## [1] ""
## [1] "pyramidal SS"
## [1] 0
## [1] "Fold enrichment: 1.24214820357332"
## [1] "Standard deviations from mean: 4.08714486088249"
## [1] ""
cell.type1 <-"astrocytes-ependymal"
cell.type2 <-"oligodendrocytes"
cell.type3 <-"interneurons"
enrichment.summary <-data.frame()
for(region in region_list){
# 1st cell type
if(subset(tt_results[[region]]$joint_results, CellType==cell.type1 &
Direction=="Up")$p<=0.05){enriched1="Yes"} else{enriched1="No"}
# 2nd cell type
if(subset(tt_results[[region]]$joint_results, CellType==cell.type2 &
Direction=="Up")$p<=0.05){enriched2="Yes"} else{enriched2="No"}
# 3rd cell type
if(subset(tt_results[[region]]$joint_results, CellType==cell.type3 &
Direction=="Up")$p<=0.05){enriched3="Yes"} else{enriched3="No"}
# Summarise
enrichment.summary <-rbind(enrichment.summary,
data.frame(Region=region, Cell.Type=cell.type1, Enriched=enriched1),
data.frame(Region=region, Cell.Type=cell.type2, Enriched=enriched2),
data.frame(Region=region, Cell.Type=cell.type3, Enriched=enriched3))
}
# Get only significantly enriched regions
enrich.summ <-subset(enrichment.summary, Enriched=="Yes")
pander(enrich.summ[order(enrich.summ$Region),], style='simple',justify='left')
| Region | Cell.Type | Enriched | |
|---|---|---|---|
| 10 | rCG6 | astrocytes-ependymal | Yes |
| 59 | ic | oligodendrocytes | Yes |
| 61 | IsCj | astrocytes-ependymal | Yes |
| 70 | GPe | astrocytes-ependymal | Yes |
| 71 | GPe | oligodendrocytes | Yes |
| 73 | GPi | astrocytes-ependymal | Yes |
| 74 | GPi | oligodendrocytes | Yes |
| 76 | V1-1 | astrocytes-ependymal | Yes |
| 85 | V1-4A | astrocytes-ependymal | Yes |
| 88 | V1-4B | astrocytes-ependymal | Yes |
| 94 | V14Cb | astrocytes-ependymal | Yes |
| 97 | V1-5 | astrocytes-ependymal | Yes |
| 100 | V1-6 | astrocytes-ependymal | Yes |
| 103 | V1wm | astrocytes-ependymal | Yes |
| 104 | V1wm | oligodendrocytes | Yes |
| 109 | V2-3 | astrocytes-ependymal | Yes |
| 118 | NAC | astrocytes-ependymal | Yes |
| 122 | V2-6 | oligodendrocytes | Yes |
| 124 | CA2py | astrocytes-ependymal | Yes |
| 127 | DGpf | astrocytes-ependymal | Yes |
| 130 | DGgr | astrocytes-ependymal | Yes |
| 133 | S | astrocytes-ependymal | Yes |
| 139 | CA1ra | astrocytes-ependymal | Yes |
| 142 | CA1or | astrocytes-ependymal | Yes |
| 143 | CA1or | oligodendrocytes | Yes |
| 145 | DGsg | astrocytes-ependymal | Yes |
| 148 | CA3py | astrocytes-ependymal | Yes |
| 151 | AA | astrocytes-ependymal | Yes |
| 154 | PL | astrocytes-ependymal | Yes |
| 157 | Me | astrocytes-ependymal | Yes |
| 160 | L | astrocytes-ependymal | Yes |
| 163 | APir | astrocytes-ependymal | Yes |
| 166 | rPAC | astrocytes-ependymal | Yes |
| 172 | AHA | astrocytes-ependymal | Yes |
| 175 | B | astrocytes-ependymal | Yes |
| 178 | CE | astrocytes-ependymal | Yes |
| 181 | CA4 | astrocytes-ependymal | Yes |